VideoClub / gen1
Viewer
-- Creating instances for VideoClub model
-- Creating Clients
!new Client('client1')
!client1.clientId := 1
!client1.isSuspended := false
!new Client('client2')
!client2.clientId := 2
!client2.isSuspended := true
-- Creating Rentals
!new Rental('rental1')
!rental1.date := Date('2023-10-15')
!new Rental('rental2')
!rental2.date := Date('2023-10-16')
-- Creating Cassettes and Movies
!new Movie('movie1')
!movie1.title := 'Action Adventure'
!movie1.availableCopies := 5
!movie1.genre := #Action
!new Movie('movie2')
!movie2.title := 'Laugh Out Loud'
!movie2.availableCopies := 2
!movie2.genre := #Comedy
-- Creating Series
!new Series('series1')
!series1.title := 'Epic Space Drama'
!series1.availableCopies := 3
!series1.episode := 10
-- Creating Actors
!new Actor('actor1')
!actor1.name := 'John Doe'
!new Actor('actor2')
!actor2.name := 'Jane Smith'
-- Creating associations between Client and Rentals
!insert (client1, rental1) into ClientRental
!insert (client2, rental2) into ClientRental
-- Creating associations between Rentals and Cassettes
!insert (rental1, movie1) into RentalCassette
!insert (rental1, movie2) into RentalCassette
!insert (rental2, series1) into RentalCassette
-- Creating associations between Cassettes and Actors
!insert (movie1, actor1) into CassetteActor
!insert (movie2, actor2) into CassetteActor
!insert (series1, actor1) into CassetteActor
!insert (series1, actor2) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 Given a conceptual model expressed in the UML-based Specification Environment (USE), your task is to generate valid and realistic instances that conform to the provided model. <requirements> - Instances must be syntactically correct according to the USE syntax_reference. - Avoid unnecessary comments and output the instance in plain text (i.e., not markdown). - Make sure instances fulfill all the model's constraints, and that multiplicities, relationships, and attributes are valid and realistic. - Provide multiple instances with diverse data values and structure. </requirements> <syntax_reference> Here there is a snippet showing how to create objects and set values in the specific .soil language required: -- This is a comment example -- Primitive data types: -- Integer i.e. 1, 2, 3, etc. -- Real i.e. 1.0, 21.89, 322.05556, etc. -- Boolean i.e. true or false -- String i.e. 'Hello World' -- You can create instances with the following syntax: !new <instance type>('<instance name>') -- Example: !new Client('client1') !new Store('store4') -- You can assign values to attributes for a created instance with the following syntax: !<instance name>.<attribute name> := <value> -- Example for different data types: !client1.clientId := 1 -- For Integer !client1.balance := 1123.45 -- For Real !client1.name := 'John' -- For Strings !store4.available := true -- For Boolean -- You can create associations between instances with the following syntax: !insert (<instance name1>, <instance name2>) into <association name> -- Example: !insert (client1, store4) into ClientStore -- Custom data types usage: -- dataType Location -- operations -- Location(x : Real, y : Real) -- some other operations -- end -- You can create custom data types by calling the constructor directly; in this case, the constructor of Location requires two arguments: x and y of type Real. So it can be used as follows: !store4.location := Location(14.0, 289.0) -- Enums usage: -- enum Type { Clothes, Shoes } -- Can be used as follows: !store4.type := #Clothes </syntax_reference> Create an instance for the following domain model:
<domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model> <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
-- Creating instances for VideoClub model
-- Creating Clients
!new Client('client1')
!client1.clientId := 1
!client1.isSuspended := false
!new Client('client2')
!client2.clientId := 2
!client2.isSuspended := true
-- Creating Rentals
!new Rental('rental1')
!rental1.date := Date('2023-10-15')
!new Rental('rental2')
!rental2.date := Date('2023-10-16')
-- Creating Cassettes and Movies
!new Movie('movie1')
!movie1.title := 'Action Adventure'
!movie1.availableCopies := 5
!movie1.genre := #Action
!new Movie('movie2')
!movie2.title := 'Laugh Out Loud'
!movie2.availableCopies := 2
!movie2.genre := #Comedy
-- Creating Series
!new Series('series1')
!series1.title := 'Epic Space Drama'
!series1.availableCopies := 3
!series1.episode := 10
-- Creating Actors
!new Actor('actor1')
!actor1.name := 'John Doe'
!new Actor('actor2')
!actor2.name := 'Jane Smith'
-- Creating associations between Client and Rentals
!insert (client1, rental1) into ClientRental
!insert (client2, rental2) into ClientRental
-- Creating associations between Rentals and Cassettes
!insert (rental1, movie1) into RentalCassette
!insert (rental1, movie2) into RentalCassette
!insert (rental2, series1) into RentalCassette
-- Creating associations between Cassettes and Actors
!insert (movie1, actor1) into CassetteActor
!insert (movie2, actor2) into CassetteActor
!insert (series1, actor1) into CassetteActor
!insert (series1, actor2) into CassetteActor
</object_model> LLM as a Judge
The object model represents a standard video club scenario with plausible attributes (positive copies, valid dates, realistic names and titles). The associations between clients, rentals, cassettes, and actors align logically with how a real-world video rental system operates.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.01 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/35 |
| Multiplicities | 0/9 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 93.3% |
| String Equals | 100.0% |
| String LV | 85.7% |
| Shannon (Active) | 1.000 ± 0.000 |
| Shannon (All) | 1.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 9/∞ |
| Attributes | 17/17 |
| Relationships | 9/∞ |
Viewer
!new Client('client1')
!client1.clientId := 1023
!client1.isSuspended := false
!new Rental('rental1')
!rental1.date := Date('2023-07-15')
!new Movie('movie1')
!movie1.title := 'The Adventurer\'s Quest'
!movie1.availableCopies := 5
!movie1.genre := #Action
!new Series('series1')
!series1.title := 'Comedy Nights Season 2'
!series1.availableCopies := 3
!series1.episode := 4
!new Actor('actor1')
!actor1.name := 'Liam O\'Sullivan'
!new Actor('actor2')
!actor2.name := 'Sophia Chen'
!insert (client1, rental1) into ClientRental
!insert (rental1, movie1) into RentalCassette
!insert (rental1, series1) into RentalCassette
!insert (movie1, actor1) into CassetteActor
!insert (movie1, actor2) into CassetteActor
!insert (series1, actor2) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand. You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose. ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association. ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled). Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes. You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created. Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming. <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> Analyze the following UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 # Conceptual model description:
## Description
The VideoClub model represents the structure of a video rental system, detailing classes such as clients, rentals, cassettes, and actors. This model defines the data structures and relationships necessary to manage a collection of films and series, rentals, and the individuals involved in the creation of these media.
### Components
- **Date (dataType):** Represents a date, initialized with a string.
- **Genre (enum):** Defines two possible genre types for a movie: Action and Comedy.
- **Client (class):** Represents a person renting videos, having an `clientId` (Integer) and an `isSuspended` status (Boolean).
- **Rental (class):** Represents a rental instance with a `date` (Date).
- **Cassette (class):** Represents a physical video with a `title` (String) and `availableCopies` (Integer).
- **Movie (class):** A type of Cassette with an additional `genre` (Genre).
- **Series (class):** A type of Cassette with an `episode` number (Integer).
- **Actor (class):** Represents an actor, with a `name` (String).
## Relationships
- **ClientRental Association:**
- **Client [1]:** Each client may have one or more rentals assigned to them.
- **Rental [*]:** Each rental is related to exactly one client.
- **RentalCassette Association:**
- **Rental [*]:** Each rental can involve one or more cassettes.
- **Cassette [1..*]:** Each cassette rental involves at least one cassette.
- **CassetteActor Association:**
- **Cassette [*]:** Each cassette might have multiple actors associated with it.
- **Actor [1..*]:** Each actor must be associated with at least one cassette.
## Invariants
- **Positive Client ID:** Every client's `clientId` must be greater than zero.
- **Non-Negative Available Copies:** The `availableCopies` of a cassette must be zero or more.
- **Positive Episode Number:** The `episode` number for a series must be greater than zero.
# Category: Baseline Instances
Create a baseline instance. This is an instance that represents a realistic typical/standard scenario. Ensure every class and relationship is present in the instance at least once. # UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
# Syntax example of instances creation:
-- This is a comment example
-- Primitive data types:
-- Integer i.e. 1, 2, 3, etc.
-- Real i.e. 1.0, 21.89, 322.05556, etc.
-- Boolean i.e. true or false
-- String i.e. 'Hello World'
-- You can create instances with the following syntax:
!new <instance type>('<instance name>')
-- Example:
!new Client('client1')
!new Store('store4')
-- You can assign values to attributes for a created instance with the following syntax:
!<instance name>.<attribute name> := <value>
-- Example for different data types:
!client1.clientId := 1 -- For Integer
!client1.balance := 1123.45 -- For Real
!client1.name := 'John' -- For Strings
!store4.available := true -- For Boolean
-- You can create associations between instances with the following syntax:
!insert (<instance name1>, <instance name2>) into <association name>
-- Example:
!insert (client1, store4) into ClientStore
-- Custom data types usage:
-- dataType Location
-- operations
-- Location(x : Real, y : Real)
-- some other operations
-- end
-- You can create custom data types by calling the constructor directly; in this case, the constructor of Location requires two arguments: x and y of type Real. So it can be used as follows:
!store4.location := Location(14.0, 289.0)
-- Enums usage:
-- enum Type { Clothes, Shoes }
-- Can be used as follows:
!store4.type := #Clothes
# Instance description:
Please create the instance according to this specification:
## VideoClub Baseline Instance
### Clients
- **Client:**
- **Client ID:** 1023
- **Is Suspended:** False
### Rentals
- **Rental:**
- **Date:** "2023-07-15"
### Cassettes
- **Movie Cassette:**
- **Title:** "The Adventurer's Quest"
- **Available Copies:** 5
- **Genre:** Action
- **Series Cassette:**
- **Title:** "Comedy Nights Season 2"
- **Available Copies:** 3
- **Episode:** 4
### Actors
- **Actor 1:**
- **Name:** "Liam O'Sullivan"
- **Actor 2:**
- **Name:** "Sophia Chen"
### Relationships
#### ClientRental Association
- **Client 1023:**
- **Rental Date:** "2023-07-15"
#### RentalCassette Association
- **Rental Date "2023-07-15":**
- **Cassette Titles:**
- "The Adventurer's Quest"
- "Comedy Nights Season 2"
#### CassetteActor Association
- **Movie Cassette "The Adventurer's Quest":**
- **Actors:**
- Liam O'Sullivan
- Sophia Chen
- **Series Cassette "Comedy Nights Season 2":**
- **Actors:**
- Sophia Chen
### Description
In this instance, we have a single client, not suspended, with a client ID of 1023. The client has rented both a movie and a series on the date specified as "2023-07-15". The movie "The Adventurer's Quest" belongs to the action genre and has 5 copies available. The series "Comedy Nights Season 2" has 3 copies available and the rental pertains to episode 4. The two actors, Liam O'Sullivan and Sophia Chen, are associated with the cassettes, with Liam acting in the action movie and Sophia appearing in both the movie and the comedy series. Each relationship between entities is covered, conforming to the constraints and multiplicities established in the model. The last output is partially incorrect:
temp.soil> !new Rental('rental1')
temp.soil> !rental1.date := Date("2023-07-15")
<input>:line 1:7 mismatched input '.' expecting :=
Please provide the complete output corrected <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
!new Client('client1')
!client1.clientId := 1023
!client1.isSuspended := false
!new Rental('rental1')
!rental1.date := Date('2023-07-15')
!new Movie('movie1')
!movie1.title := 'The Adventurer\'s Quest'
!movie1.availableCopies := 5
!movie1.genre := #Action
!new Series('series1')
!series1.title := 'Comedy Nights Season 2'
!series1.availableCopies := 3
!series1.episode := 4
!new Actor('actor1')
!actor1.name := 'Liam O\'Sullivan'
!new Actor('actor2')
!actor2.name := 'Sophia Chen'
!insert (client1, rental1) into ClientRental
!insert (rental1, movie1) into RentalCassette
!insert (rental1, series1) into RentalCassette
!insert (movie1, actor1) into CassetteActor
!insert (movie1, actor2) into CassetteActor
!insert (series1, actor2) into CassetteActor
</object_model> LLM as a Judge
The assigned attributes have sensible real-world values (plausible titles, actor names, valid rental date, and positive item counts), and the relationships model a completely standard video club transaction where a client rents multiple cassettes at once.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.02 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/23 |
| Multiplicities | 0/6 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 100.0% |
| String Equals | 100.0% |
| String LV | 87.2% |
| Shannon (Active) | 0.000 ± 0.000 |
| Shannon (All) | 0.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 6/∞ |
| Attributes | 11/11 |
| Relationships | 6/∞ |
Viewer
!new Client('client4')
!client4.clientId := 1
!client4.isSuspended := false
!new Rental('rental5')
!rental5.date := Date('2023-12-01')
!new Movie('movie4')
!movie4.title := 'Silent Action'
!movie4.availableCopies := 1
!movie4.genre := #Action
!new Series('series4')
!series4.title := 'Fun Times'
!series4.availableCopies := 0
!series4.episode := 1
!new Actor('actor7')
!actor7.name := 'John Doe'
!insert (client4, rental5) into ClientRental
!insert (rental5, movie4) into RentalCassette
!insert (rental5, series4) into RentalCassette
!insert (movie4, actor7) into CassetteActor
!insert (series4, actor7) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand. You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose. ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association. ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled). Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes. You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created. Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming. <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> Analyze the following UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 # Conceptual model description:
## Description
The VideoClub model represents the structure of a video rental system, detailing classes such as clients, rentals, cassettes, and actors. This model defines the data structures and relationships necessary to manage a collection of films and series, rentals, and the individuals involved in the creation of these media.
### Components
- **Date (dataType):** Represents a date, initialized with a string.
- **Genre (enum):** Defines two possible genre types for a movie: Action and Comedy.
- **Client (class):** Represents a person renting videos, having an `clientId` (Integer) and an `isSuspended` status (Boolean).
- **Rental (class):** Represents a rental instance with a `date` (Date).
- **Cassette (class):** Represents a physical video with a `title` (String) and `availableCopies` (Integer).
- **Movie (class):** A type of Cassette with an additional `genre` (Genre).
- **Series (class):** A type of Cassette with an `episode` number (Integer).
- **Actor (class):** Represents an actor, with a `name` (String).
## Relationships
- **ClientRental Association:**
- **Client [1]:** Each client may have one or more rentals assigned to them.
- **Rental [*]:** Each rental is related to exactly one client.
- **RentalCassette Association:**
- **Rental [*]:** Each rental can involve one or more cassettes.
- **Cassette [1..*]:** Each cassette rental involves at least one cassette.
- **CassetteActor Association:**
- **Cassette [*]:** Each cassette might have multiple actors associated with it.
- **Actor [1..*]:** Each actor must be associated with at least one cassette.
## Invariants
- **Positive Client ID:** Every client's `clientId` must be greater than zero.
- **Non-Negative Available Copies:** The `availableCopies` of a cassette must be zero or more.
- **Positive Episode Number:** The `episode` number for a series must be greater than zero.
# Category: Boundary Instances
Create a boundary case instance. This is an instance that focuses on the extreme upper or lower limits of valid input ranges. For example:
- Upper or lower limits of multiplicities.
- For numbers in a range, the minimum and maximum valid values.
- Empty collections when possible, i.e., when they do not violate the semantics of the model or its constraints. Continue with the following description, creating the instance according to the syntax example and this specification:
In this boundary instance for the VideoClub model, we aim to explore the limits of the defined constraints, paying particular attention to the extreme values in various components:
### Instance Description
#### Clients
- **Client1**: Named "Alice Johnson", with a `clientId` of the smallest valid positive integer, which is 1, and an `isSuspended` status of `false`. Alice has no rentals at the moment (reflecting a potential minimum multiplicity within a practical boundary of client interactions).
#### Rentals
- **Rental1**: Dated "2023-12-01". It is associated with two cassettes, reflecting boundary conditions for an association where each rental should involve at least one cassette.
#### Cassettes
- **Movie1**: Titled "Silent Action", belonging to the `Action` genre, with an `availableCopies` set to 1, representing the minimum allowable non-zero value.
- **Series1**: Titled "Fun Times", having an `episode` number of 1 (the smallest valid episode number), and `availableCopies` of 0, showcasing a boundary condition where there are no available copies for rent but the series still exists within the catalog.
#### Actors
- **Actor1**: Named "John Doe", who is associated with both "Silent Action" and "Fun Times". This reflects the boundary condition where one actor is the minimal common associate to multiple classifications of media: movies and series.
### Relationships and Structure
- Alice Johnson currently has no active rentals, showcasing a valid instance where a client doesn't maintain any rented media at the moment but retains a valid status within the system.
- Rental1 is associated with both a movie and a series, demonstrating the flexibility and variety possible within a single rental instance. Here, the rental involves more than the minimum required cassette association, pushing towards a potential upper boundary within a simple context.
- John Doe performs in two distinct productions, emphasizing the boundary where an individual actor is linked to multiple media types, stressing the upper end of actor-to-cassette multiplicity to some extent.
### Semantic Considerations
This boundary instance is structurally diverse in its minimal approach for some elements while exploring upper boundaries within others. It captures a practical operational setting where the video library could still function with minimal inventory and diversified associations, demonstrating the system's robustness to handle edge conditions effectively. The last output is partially incorrect:
checking structure...
Multiplicity constraint violation in association `ClientRental':
Object `rental5' of class `Rental' is connected to 0 objects of class `Client'
at association end `assigned' but the multiplicity is specified as `1'.
Please provide the complete output corrected <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
!new Client('client4')
!client4.clientId := 1
!client4.isSuspended := false
!new Rental('rental5')
!rental5.date := Date('2023-12-01')
!new Movie('movie4')
!movie4.title := 'Silent Action'
!movie4.availableCopies := 1
!movie4.genre := #Action
!new Series('series4')
!series4.title := 'Fun Times'
!series4.availableCopies := 0
!series4.episode := 1
!new Actor('actor7')
!actor7.name := 'John Doe'
!insert (client4, rental5) into ClientRental
!insert (rental5, movie4) into RentalCassette
!insert (rental5, series4) into RentalCassette
!insert (movie4, actor7) into CassetteActor
!insert (series4, actor7) into CassetteActor
</object_model> LLM as a Judge
The object model uses plausible attribute values (a valid date, positive client ID, standard actor name, and appropriate genres/episodes). The relationships are logical, representing a client renting a movie and a series that share a common actor, which is a common real-world scenario in a video club.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.03 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/20 |
| Multiplicities | 0/5 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 50.0% |
| String Equals | 100.0% |
| String LV | 79.8% |
| Shannon (Active) | 0.000 ± 0.000 |
| Shannon (All) | 0.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 5/∞ |
| Attributes | 10/10 |
| Relationships | 5/∞ |
Viewer
!new Client('client2')
!client2.clientId := 102
!client2.isSuspended := false
!new Client('client3')
!client3.clientId := 205
!client3.isSuspended := true
!new Rental('rental2')
!rental2.date := Date('2023-09-05')
!new Rental('rental3')
!rental3.date := Date('2023-10-15')
!new Rental('rental4')
!rental4.date := Date('2023-11-01')
!new Movie('movie2')
!movie2.title := 'The Great Escape'
!movie2.availableCopies := 5
!movie2.genre := #Action
!new Movie('movie3')
!movie3.title := 'Laugh Out Loud'
!movie3.availableCopies := 2
!movie3.genre := #Comedy
!new Series('series2')
!series2.title := 'Mystery of the East'
!series2.availableCopies := 4
!series2.episode := 3
!new Series('series3')
!series3.title := 'Urban Legends'
!series3.availableCopies := 6
!series3.episode := 7
!new Actor('actor3')
!actor3.name := 'Tom Hardy'
!new Actor('actor4')
!actor4.name := 'Maya Lopez'
!new Actor('actor5')
!actor5.name := 'John Smith'
!new Actor('actor6')
!actor6.name := 'Priya Patel'
!insert (client2, rental2) into ClientRental
!insert (client2, rental3) into ClientRental
!insert (client3, rental4) into ClientRental
!insert (rental2, movie2) into RentalCassette
!insert (rental2, series2) into RentalCassette
!insert (rental3, movie3) into RentalCassette
!insert (rental4, series3) into RentalCassette
!insert (movie2, actor3) into CassetteActor
!insert (movie2, actor4) into CassetteActor
!insert (movie3, actor4) into CassetteActor
!insert (movie3, actor5) into CassetteActor
!insert (series2, actor6) into CassetteActor
!insert (series2, actor5) into CassetteActor
!insert (series3, actor6) into CassetteActor
!insert (series3, actor3) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand. You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose. ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association. ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled). Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes. You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created. Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming. <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> Analyze the following UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 # Conceptual model description:
## Description
The VideoClub model represents the structure of a video rental system, detailing classes such as clients, rentals, cassettes, and actors. This model defines the data structures and relationships necessary to manage a collection of films and series, rentals, and the individuals involved in the creation of these media.
### Components
- **Date (dataType):** Represents a date, initialized with a string.
- **Genre (enum):** Defines two possible genre types for a movie: Action and Comedy.
- **Client (class):** Represents a person renting videos, having an `clientId` (Integer) and an `isSuspended` status (Boolean).
- **Rental (class):** Represents a rental instance with a `date` (Date).
- **Cassette (class):** Represents a physical video with a `title` (String) and `availableCopies` (Integer).
- **Movie (class):** A type of Cassette with an additional `genre` (Genre).
- **Series (class):** A type of Cassette with an `episode` number (Integer).
- **Actor (class):** Represents an actor, with a `name` (String).
## Relationships
- **ClientRental Association:**
- **Client [1]:** Each client may have one or more rentals assigned to them.
- **Rental [*]:** Each rental is related to exactly one client.
- **RentalCassette Association:**
- **Rental [*]:** Each rental can involve one or more cassettes.
- **Cassette [1..*]:** Each cassette rental involves at least one cassette.
- **CassetteActor Association:**
- **Cassette [*]:** Each cassette might have multiple actors associated with it.
- **Actor [1..*]:** Each actor must be associated with at least one cassette.
## Invariants
- **Positive Client ID:** Every client's `clientId` must be greater than zero.
- **Non-Negative Available Copies:** The `availableCopies` of a cassette must be zero or more.
- **Positive Episode Number:** The `episode` number for a series must be greater than zero.
# Category: Complex Instances
Create a complex instance that is realistic and contains multiple interrelated entities and/or entities that are involved in multiple constraints. Continue with the following description, creating the instance according to the syntax example and this specification:
## Complex VideoClub Instance
### Entities
#### Clients
1. **Client:**
- **clientId:** 102
- **isSuspended:** False
2. **Client:**
- **clientId:** 205
- **isSuspended:** True
#### Rentals
1. **Rental:**
- **date:** "2023-09-05"
2. **Rental:**
- **date:** "2023-10-15"
3. **Rental:**
- **date:** "2023-11-01"
#### Cassettes
1. **Movie:**
- **title:** "The Great Escape"
- **availableCopies:** 5
- **genre:** Action
2. **Movie:**
- **title:** "Laugh Out Loud"
- **availableCopies:** 2
- **genre:** Comedy
3. **Series:**
- **title:** "Mystery of the East"
- **availableCopies:** 4
- **episode:** 3
4. **Series:**
- **title:** "Urban Legends"
- **availableCopies:** 6
- **episode:** 7
#### Actors
1. **Actor:**
- **name:** "Tom Hardy"
2. **Actor:**
- **name:** "Maya Lopez"
3. **Actor:**
- **name:** "John Smith"
4. **Actor:**
- **name:** "Priya Patel"
### Relationships
#### ClientRental Association
- **Client 102** has the following rentals:
- **Rental on 2023-09-05**
- Involves:
- **The Great Escape**
- **Mystery of the East**
- **Rental on 2023-10-15**
- Involves:
- **Laugh Out Loud**
- **Client 205** has the following rentals:
- **Rental on 2023-11-01**
- Involves:
- **Urban Legends**
#### RentalCassette Association
- **Rental on 2023-09-05** involves:
- **Cassette:** The Great Escape
- **Cassette:** Mystery of the East
- **Rental on 2023-10-15** involves:
- **Cassette:** Laugh Out Loud
- **Rental on 2023-11-01** involves:
- **Cassette:** Urban Legends
#### CassetteActor Association
- **The Great Escape** includes actors:
- Tom Hardy
- Maya Lopez
- **Laugh Out Loud** includes actors:
- Maya Lopez
- John Smith
- **Mystery of the East** includes actors:
- Priya Patel
- John Smith
- **Urban Legends** includes actors:
- Priya Patel
- Tom Hardy
This instance is structured to demonstrate multiple relationships across different classes, showcasing interconnections between clients, rentals, cassettes, and actors with diverse attributes and scenarios. <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
!new Client('client2')
!client2.clientId := 102
!client2.isSuspended := false
!new Client('client3')
!client3.clientId := 205
!client3.isSuspended := true
!new Rental('rental2')
!rental2.date := Date('2023-09-05')
!new Rental('rental3')
!rental3.date := Date('2023-10-15')
!new Rental('rental4')
!rental4.date := Date('2023-11-01')
!new Movie('movie2')
!movie2.title := 'The Great Escape'
!movie2.availableCopies := 5
!movie2.genre := #Action
!new Movie('movie3')
!movie3.title := 'Laugh Out Loud'
!movie3.availableCopies := 2
!movie3.genre := #Comedy
!new Series('series2')
!series2.title := 'Mystery of the East'
!series2.availableCopies := 4
!series2.episode := 3
!new Series('series3')
!series3.title := 'Urban Legends'
!series3.availableCopies := 6
!series3.episode := 7
!new Actor('actor3')
!actor3.name := 'Tom Hardy'
!new Actor('actor4')
!actor4.name := 'Maya Lopez'
!new Actor('actor5')
!actor5.name := 'John Smith'
!new Actor('actor6')
!actor6.name := 'Priya Patel'
!insert (client2, rental2) into ClientRental
!insert (client2, rental3) into ClientRental
!insert (client3, rental4) into ClientRental
!insert (rental2, movie2) into RentalCassette
!insert (rental2, series2) into RentalCassette
!insert (rental3, movie3) into RentalCassette
!insert (rental4, series3) into RentalCassette
!insert (movie2, actor3) into CassetteActor
!insert (movie2, actor4) into CassetteActor
!insert (movie3, actor4) into CassetteActor
!insert (movie3, actor5) into CassetteActor
!insert (series2, actor6) into CassetteActor
!insert (series2, actor5) into CassetteActor
!insert (series3, actor6) into CassetteActor
!insert (series3, actor3) into CassetteActor
</object_model> LLM as a Judge
The object model represents a standard system state for a video club with plausible instances of movies, series, clients, and actors. The attribute values (e.g., sensible titles, logical dates, proper names, and positive copy quantities) map logically to real-world entities, and the relationships appropriately reflect valid rental transactions.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.02 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/51 |
| Multiplicities | 0/15 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 100.0% |
| String Equals | 100.0% |
| String LV | 84.1% |
| Shannon (Active) | 1.000 ± 0.000 |
| Shannon (All) | 1.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 13/∞ |
| Attributes | 23/23 |
| Relationships | 15/∞ |
Viewer
!new Client('client6')
!client6.clientId := 2147483647
!client6.isSuspended := false
!new Rental('rental7')
!rental7.date := Date('2023-10-15')
!new Movie('movie7')
!movie7.title := 'Zorros of the Sahara'
!movie7.availableCopies := 0
!movie7.genre := #Action
!new Movie('movie8')
!movie8.title := 'Laugh Like a Local'
!movie8.availableCopies := 999999999
!movie8.genre := #Comedy
!new Series('series6')
!series6.title := 'Tommy\'s Tournament'
!series6.availableCopies := 1
!series6.episode := 32768
!new Actor('actor11')
!actor11.name := 'Pedro Lopez'
!new Actor('actor12')
!actor12.name := 'Ayesha Rai'
!new Actor('actor13')
!actor13.name := 'Aiden O\'Connell'
!new Actor('actor14')
!actor14.name := 'Sasha Kimura'
!new Actor('actor15')
!actor15.name := 'Lee Min-ho'
!insert (client6, rental7) into ClientRental
!insert (rental7, movie7) into RentalCassette
!insert (rental7, movie8) into RentalCassette
!insert (rental7, series6) into RentalCassette
!insert (movie7, actor11) into CassetteActor
!insert (movie7, actor12) into CassetteActor
!insert (movie8, actor13) into CassetteActor
!insert (series6, actor14) into CassetteActor
!insert (series6, actor15) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand. You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose. ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association. ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled). Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes. You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created. Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming. <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> Analyze the following UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 # Conceptual model description:
## Description
The VideoClub model represents the structure of a video rental system, detailing classes such as clients, rentals, cassettes, and actors. This model defines the data structures and relationships necessary to manage a collection of films and series, rentals, and the individuals involved in the creation of these media.
### Components
- **Date (dataType):** Represents a date, initialized with a string.
- **Genre (enum):** Defines two possible genre types for a movie: Action and Comedy.
- **Client (class):** Represents a person renting videos, having an `clientId` (Integer) and an `isSuspended` status (Boolean).
- **Rental (class):** Represents a rental instance with a `date` (Date).
- **Cassette (class):** Represents a physical video with a `title` (String) and `availableCopies` (Integer).
- **Movie (class):** A type of Cassette with an additional `genre` (Genre).
- **Series (class):** A type of Cassette with an `episode` number (Integer).
- **Actor (class):** Represents an actor, with a `name` (String).
## Relationships
- **ClientRental Association:**
- **Client [1]:** Each client may have one or more rentals assigned to them.
- **Rental [*]:** Each rental is related to exactly one client.
- **RentalCassette Association:**
- **Rental [*]:** Each rental can involve one or more cassettes.
- **Cassette [1..*]:** Each cassette rental involves at least one cassette.
- **CassetteActor Association:**
- **Cassette [*]:** Each cassette might have multiple actors associated with it.
- **Actor [1..*]:** Each actor must be associated with at least one cassette.
## Invariants
- **Positive Client ID:** Every client's `clientId` must be greater than zero.
- **Non-Negative Available Copies:** The `availableCopies` of a cassette must be zero or more.
- **Positive Episode Number:** The `episode` number for a series must be greater than zero.
# Category: Edge Instances
Create an edge case instance. This is an instance that behaves within but at the limit of the expected behavior. This instance must focus on a scenario that is unusual or unlikely in real life but possible according to the syntax and semantics of the model. In terms of semantics, take into account constraints, multiplicities, and uncommon combinations of relationships and attributes. Continue with the following description, creating the instance according to the syntax example and this specification:
### Edge Case Instance: The Global Cassette Renter Scenario
#### Date Instance
- **Date:** "2023-10-15"
#### Client Class
1. **Name:** Amina Falana
- **clientId:** 2147483647 (Using a very large positive integer close to the maximum for `Integer`)
- **isSuspended:** False
- **Rentals:**
#### Rental Class
1. **Rental Date:** "2023-10-15"
- **Associated Client:** Amina Falana
- **Cassettes Involved:**
- "Zorros of the Sahara"
- "Laugh Like a Local"
- "Tommy's Tournament"
#### Cassette and Derived Classes
1. **Movie:**
- **Title:** "Zorros of the Sahara"
- **availableCopies:** 0 (An unusual but valid condition, possibly indicating a sold-out title)
- **genre:** Action
- **Associated Actors:**
- Pedro Lopez
- Ayesha Rai
2. **Movie:**
- **Title:** "Laugh Like a Local"
- **availableCopies:** 999999999 (A very large number indicating a massive quantity in stock)
- **genre:** Comedy
- **Associated Actors:**
- Aiden O’Connell
3. **Series:**
- **Title:** "Tommy's Tournament"
- **availableCopies:** 1 (Just enough to rent, suggesting rarity or a new release)
- **episode:** 32768 (A large episode number, suggesting a very long-running series)
- **Associated Actors:**
- Sasha Kimura
- Lee Min-ho
#### Actor Class
1. **Actor Name:** Pedro Lopez
- **Associated Cassettes:**
- "Zorros of the Sahara"
2. **Actor Name:** Ayesha Rai
- **Associated Cassettes:**
- "Zorros of the Sahara"
3. **Actor Name:** Aiden O’Connell
- **Associated Cassettes:**
- "Laugh Like a Local"
4. **Actor Name:** Sasha Kimura
- **Associated Cassettes:**
- "Tommy's Tournament"
5. **Actor Name:** Lee Min-ho
- **Associated Cassettes:**
- "Tommy's Tournament"
#### Description:
This "Global Cassette Renter Scenario" showcases some edge conditions like a client with a maximum integer `clientId`, zero available copies because a movie may be very popular, a cassette with unrealistically high stocks suggesting it's either a globally utilized and distributed title or a digital listing unmanaged conventionally, and a massive episode count for a series, indicating its enduring popularity or a show that broke viewing and episode production norms.
This instance also displays unique cultural and geographic diversity by having multifaceted actors like Pedro Lopez, Ayesha Rai, Aiden O’Connell, Sasha Kimura, and Lee Min-ho, reflecting a broad range of ethnicities and potentially international cinematic universes. <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
!new Client('client6')
!client6.clientId := 2147483647
!client6.isSuspended := false
!new Rental('rental7')
!rental7.date := Date('2023-10-15')
!new Movie('movie7')
!movie7.title := 'Zorros of the Sahara'
!movie7.availableCopies := 0
!movie7.genre := #Action
!new Movie('movie8')
!movie8.title := 'Laugh Like a Local'
!movie8.availableCopies := 999999999
!movie8.genre := #Comedy
!new Series('series6')
!series6.title := 'Tommy\'s Tournament'
!series6.availableCopies := 1
!series6.episode := 32768
!new Actor('actor11')
!actor11.name := 'Pedro Lopez'
!new Actor('actor12')
!actor12.name := 'Ayesha Rai'
!new Actor('actor13')
!actor13.name := 'Aiden O\'Connell'
!new Actor('actor14')
!actor14.name := 'Sasha Kimura'
!new Actor('actor15')
!actor15.name := 'Lee Min-ho'
!insert (client6, rental7) into ClientRental
!insert (rental7, movie7) into RentalCassette
!insert (rental7, movie8) into RentalCassette
!insert (rental7, series6) into RentalCassette
!insert (movie7, actor11) into CassetteActor
!insert (movie7, actor12) into CassetteActor
!insert (movie8, actor13) into CassetteActor
!insert (series6, actor14) into CassetteActor
!insert (series6, actor15) into CassetteActor
</object_model> LLM as a Judge
The object 'movie8' has 999,999,999 available copies, which is physically impossible for a single video club to hold. Additionally, the object 'series6' is assigned an episode number of 32,768, which far exceeds the episode count of any real-world television series.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.03 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/36 |
| Multiplicities | 0/9 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 100.0% |
| String Equals | 100.0% |
| String LV | 87.0% |
| Shannon (Active) | 1.000 ± 0.000 |
| Shannon (All) | 1.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 10/∞ |
| Attributes | 17/17 |
| Relationships | 9/∞ |
Viewer
!new Client('client5')
!client5.clientId := 42
!client5.isSuspended := false
!new Rental('rental6')
!rental6.date := Date('2023-09-22')
!new Movie('movie5')
!movie5.title := 'The Balkans\' Beat'
!movie5.availableCopies := 5
!movie5.genre := #Action
!new Movie('movie6')
!movie6.title := 'Sarajevo Romance'
!movie6.availableCopies := 1
!movie6.genre := #Comedy
!new Series('series5')
!series5.title := 'Old Balkans Tales'
!series5.availableCopies := 10
!series5.episode := 3
!new Actor('actor8')
!actor8.name := 'Nikola Petrovic'
!new Actor('actor9')
!actor9.name := 'Ana Jovanović'
!new Actor('actor10')
!actor10.name := 'Ibrahim Sinanović'
!insert (client5, rental6) into ClientRental
!insert (rental6, movie5) into RentalCassette
!insert (rental6, movie6) into RentalCassette
!insert (rental6, series5) into RentalCassette
!insert (movie5, actor8) into CassetteActor
!insert (movie5, actor9) into CassetteActor
!insert (movie6, actor9) into CassetteActor
!insert (series5, actor10) into CassetteActor
!insert (series5, actor8) into CassetteActor model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand. You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose. ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association. ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled). Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes. You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created. Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming. <role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).
Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>
<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).
Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>
<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>
<instructions>
Follow this thinking process strictly before generating the final output:
1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
- Apply "Common Sense Knowledge" to the attribute values.
- Check cardinality and relationship logic beyond simple OCL constraints.
- Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>
<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>
<output_format>
Structure your response exactly as follows:
**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>
<examples>
Example 1:
Input:
<domain_model>
class Person
attributes
age: Integer
end
class Pet
attributes
name: String
end
association Ownership between
Person [1] role owner
Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>
Output:
**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.
Example 2:
Input:
<domain_model>
class Car
attributes
brand: String
end
class Person
attributes
name: String
end
association Ownership between
Person [1] role owner
Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>
Output:
**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.
Example 3:
Input:
<domain_model>
class Component
attributes
setting_val: Integer
config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>
Output:
**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.
</examples> Analyze the following UML class diagram:
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0 # Conceptual model description:
## Description
The VideoClub model represents the structure of a video rental system, detailing classes such as clients, rentals, cassettes, and actors. This model defines the data structures and relationships necessary to manage a collection of films and series, rentals, and the individuals involved in the creation of these media.
### Components
- **Date (dataType):** Represents a date, initialized with a string.
- **Genre (enum):** Defines two possible genre types for a movie: Action and Comedy.
- **Client (class):** Represents a person renting videos, having an `clientId` (Integer) and an `isSuspended` status (Boolean).
- **Rental (class):** Represents a rental instance with a `date` (Date).
- **Cassette (class):** Represents a physical video with a `title` (String) and `availableCopies` (Integer).
- **Movie (class):** A type of Cassette with an additional `genre` (Genre).
- **Series (class):** A type of Cassette with an `episode` number (Integer).
- **Actor (class):** Represents an actor, with a `name` (String).
## Relationships
- **ClientRental Association:**
- **Client [1]:** Each client may have one or more rentals assigned to them.
- **Rental [*]:** Each rental is related to exactly one client.
- **RentalCassette Association:**
- **Rental [*]:** Each rental can involve one or more cassettes.
- **Cassette [1..*]:** Each cassette rental involves at least one cassette.
- **CassetteActor Association:**
- **Cassette [*]:** Each cassette might have multiple actors associated with it.
- **Actor [1..*]:** Each actor must be associated with at least one cassette.
## Invariants
- **Positive Client ID:** Every client's `clientId` must be greater than zero.
- **Non-Negative Available Copies:** The `availableCopies` of a cassette must be zero or more.
- **Positive Episode Number:** The `episode` number for a series must be greater than zero.
# Category: Overconstraint Detection
Create an instance that represents a real-life scenario that is logically valid but violates the model's multiplicities or constraints, exposing overly restrictive or unrealistic restrictions. Continue with the following description, creating the instance according to the syntax example and this specification:
**Overconstrained Scenario: International Film Festival Special Collection**
In this instance, we examine a special case involving independent films from the Balkans to highlight potential oversights or overly narrow constraints in the VideoClub model.
**Entities:**
1. **Client:**
- `clientId`: 42
- `isSuspended`: False
2. **Rental:**
- `date`: "2023-09-22"
3. **Movies:**
- Title: "The Balkans' Beat"
- `availableCopies`: 5
- `genre`: "Action"
- Title: "Sarajevo Romance"
- `availableCopies`: 1
- `genre`: "Comedy"
4. **Series:**
- Title: "Old Balkans Tales"
- `availableCopies`: 10
- `episode`: 3
5. **Actors:**
- Name: "Nikola Petrovic"
- Name: "Ana Jovanović"
- Name: "Ibrahim Sinanović"
**Situation Overview:**
- **ClientRental Association:**
- **Client (1):** The client with `clientId` 42.
- **Rental (0):** The client has not rented any cassettes yet, showing that a client without any rentals is possible but not covered under the current constraints, which imply at least one rental per client.
- **RentalCassette Association:**
- **Rental (0):** Without rentals, no cassette can be linked, showcasing that the mandatory link between rentals and a client does not reflect real-life scenarios where clients might exist in the database without having an active or past rental.
- **Cassette (1):** Each rental must involve at least one cassette, but in this insightfully constrained scenario, a festival may allow viewing on the premises without taking any cassettes out.
- **CassetteActor Association:**
- **Movie: "The Balkans' Beat" and "Sarajevo Romance" with no Actors:** Although actors are crucial for films, festival specials sometimes include documentaries with original footage or voiceovers, which can reflect a lack of association in the model but might bypass typical casting, exposing the unrealistic requirement that each cassette must have at least one actor.
**Model Violation Highlights:**
- **Zero Casettes Linked To Rentals:** Realistically possible for showcasing events but violates the `Cassette [1..*]`.
- **No Rentals For Active Clients:** Exhibits need for allowance of clients without rentals in database.
- **Actor-Free Productions:** Challenges the assumption that every movie production has actors visibly involved, useful in certain documentaries or special releases that prioritize storytelling without traditional acting roles.
This scenario uniquely underscores the potential for additional flexibility in the real-world functioning of a video rental system, especially for special thematic series or events, beyond strict model multiplicity constraints. <domain_model>
model VideoClub
dataType Date
operations
Date(date : String)
end
enum Genre {
Action,
Comedy
}
class Client
attributes
clientId : Integer
isSuspended : Boolean
end
class Rental
attributes
date : Date
end
class Cassette
attributes
title : String
availableCopies : Integer
end
class Movie < Cassette
attributes
genre : Genre
end
class Series < Cassette
attributes
episode : Integer
end
class Actor
attributes
name : String
end
association ClientRental between
Client [1] role assigned
Rental [*] role gets
end
association RentalCassette between
Rental [*] role borrowed
Cassette [1..*] role borrows
end
association CassetteActor between
Cassette [*] role actsIn
Actor [1..*] role has
end
constraints
context Client inv positiveClientId:
self.clientId > 0
context Cassette inv postiveAvailableCopies:
self.availableCopies >= 0
context Series inv positiveEpisode:
self.episode > 0
</domain_model>
<object_model>
!new Client('client5')
!client5.clientId := 42
!client5.isSuspended := false
!new Rental('rental6')
!rental6.date := Date('2023-09-22')
!new Movie('movie5')
!movie5.title := 'The Balkans\' Beat'
!movie5.availableCopies := 5
!movie5.genre := #Action
!new Movie('movie6')
!movie6.title := 'Sarajevo Romance'
!movie6.availableCopies := 1
!movie6.genre := #Comedy
!new Series('series5')
!series5.title := 'Old Balkans Tales'
!series5.availableCopies := 10
!series5.episode := 3
!new Actor('actor8')
!actor8.name := 'Nikola Petrovic'
!new Actor('actor9')
!actor9.name := 'Ana Jovanović'
!new Actor('actor10')
!actor10.name := 'Ibrahim Sinanović'
!insert (client5, rental6) into ClientRental
!insert (rental6, movie5) into RentalCassette
!insert (rental6, movie6) into RentalCassette
!insert (rental6, series5) into RentalCassette
!insert (movie5, actor8) into CassetteActor
!insert (movie5, actor9) into CassetteActor
!insert (movie6, actor9) into CassetteActor
!insert (series5, actor10) into CassetteActor
!insert (series5, actor8) into CassetteActor
</object_model> LLM as a Judge
The object model represents a highly plausible scenario for a video club. Attribute values such as client IDs, available copies, and episode numbers are within natural, feasible bounds. Furthermore, the fictional movie titles and actor names share a consistent regional (Balkan) theme, and the relationships appropriately link a client to a rental that includes a reasonable number of cassettes featuring corresponding actors.
Metrics
Stats
Stats Breakdown of the total cost and elapsed time for generating the instances. - Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
- Elapsed Time = Console Time (ie. Processing Time + API Calls)
- Cost = (input tokens * input price) + (output tokens * output price)
| Total Cost | $0.02 |
Validation
Validation Measures the correctness of the instantiation using the USE check function. - Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
- Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
- Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
- Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
| Syntax | 0/32 |
| Multiplicities | 0/9 |
| Invariants | 0/3 |
Diversity
Diversity Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values. - NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
- NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
- StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
- StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
- GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
- Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
- Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
| Numeric | 100.0% |
| String Equals | 100.0% |
| String LV | 81.3% |
| Shannon (Active) | 1.000 ± 0.000 |
| Shannon (All) | 1.000 ± 0.000 |
Coverage
Model Coverage Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?" - Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
- Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
- Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
- Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
| Classes | 83.3% |
| Attributes | 83.3% |
| Relationships | 100.0% |
Uncovered Items 3
Instantiation
Instance Instantiation Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?" - Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
- Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
- Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
- Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
| Classes | 8/∞ |
| Attributes | 15/15 |
| Relationships | 9/∞ |