Dashboard
Global Summary
Simple
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)
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)
| Total Cost | $14.26 |
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))
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 | 100.0% |
| Multiplicities | 100.0% |
| Invariants | 99.8% |
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.
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.
| Numeric | 98.9% |
| String Equals | 100.0% |
| String LV | 91.2% |
| GED | 0.892 ± 0.077 |
| Shannon (Active) | 0.865 ± 0.244 |
| Shannon (All) | 0.841 ± 0.255 |
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
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 | 86.3% |
| Attributes | 88.3% |
| Relationships | 93.8% |
Uncovered Items 67
Classes 14
BanquetBusDriverCassetteDietaryRequirementFreeRoomTypesDTOIndividualManagerMatchNote
Show all 14 classes
BanquetBusDriverCassetteDietaryRequirementFreeRoomTypesDTOIndividualManagerMatchNotePersonPlayerNotesReportedAllergySeriesTrainingFailedToAttendVehicle
Attributes 47
Banquet.busServiceBanquet.dateBanquet.groupNameBanquet.nameBanquet.numberBanquet.numberPeopleBanquet.paymentMethodBanquet.phoneNumber
Show all 47 attributes
Banquet.busServiceBanquet.dateBanquet.groupNameBanquet.nameBanquet.numberBanquet.numberPeopleBanquet.paymentMethodBanquet.phoneNumberBanquet.timeBusDriver.dateOfBirthBusDriver.driverLicenseNrBusDriver.nameBusDriver.phoneNumberCassette.availableCopiesCassette.titleDietaryRequirement.dietFreeRoomTypesDTO.numBedsFreeRoomTypesDTO.numFreeRoomsFreeRoomTypesDTO.pricePerNightFreeRoomTypesDTO.roomTypeDescriptionIndividual.dateIndividual.nameIndividual.numberIndividual.numberPeopleIndividual.phoneNumberIndividual.seatingIndividual.smokingIndividual.timeManager.dateOfBirthManager.nameManager.phoneNumberMatchNote.dateMatchNote.notePerson.namePlayerNotes.datePlayerNotes.noteReportedAllergy.allergenSeries.availableCopiesSeries.episodeSeries.titleTrainingFailedToAttend.reasonVehicle.expirationDateVehicle.idVehicle.licensePlateNumberVehicle.registrationLastMaintenanceDateVehicle.registrationStateVehicle.vehicleTypeCode
Relationships 6
BanquetBusDriverFailedPlayerMatchMatchNotePlayerPlayerNotesReservationCustomerTrainingFailded
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)
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 | 3369/∞ |
| Attributes | 9096/9096 |
| Relationships | 3978/∞ |
Quality
Quality Measures the realism of the generated instances. It identifies how much the LLM respects real-world logic. Using Gemini 3.1 Pro as an LLM as a Judge we ask it to rate (realistic, unrealistic, doubtful) the realism of each instance and explain its decision. - Realism = Total Number of "realistic" instances / Total Number of instances
- Judge Cost = (input tokens * input price) + (output tokens * output price)
Quality
Measures the realism of the generated instances. It identifies how much the LLM respects real-world logic. Using Gemini 3.1 Pro as an LLM as a Judge we ask it to rate (realistic, unrealistic, doubtful) the realism of each instance and explain its decision.
- Realism = Total Number of "realistic" instances / Total Number of instances
- Judge Cost = (input tokens * input price) + (output tokens * output price)
| Realism | 78.0% |
| Judge Cost | $5.85 |
CoT
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)
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)
| Total Cost | $58.80 |
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))
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 | 99.9% |
| Multiplicities | 99.4% |
| Invariants | 99.2% |
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.
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.
| Numeric | 98.4% |
| String Equals | 100.0% |
| String LV | 91.3% |
| GED | 0.598 ± 0.115 |
| Shannon (Active) | 0.891 ± 0.108 |
| Shannon (All) | 0.868 ± 0.154 |
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
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 | 86.0% |
| Attributes | 83.7% |
| Relationships | 91.7% |
Uncovered Items 172
Classes 32
AddressAllergenBanquetBusDriverCassetteChefCommentCompany
Show all 32 classes
AddressAllergenBanquetBusDriverCassetteChefCommentCompanyCompetitionCookDietaryRequirementFoodItemFreeRoomTypesDTOGeoLocationHeadWaiterIndividualItemOrderManagerMatchNoteMatchPlayerPositionMenuItemPersonPlayerNotesRegularCustomerReportedAllergySeriesShipmentTrainingFailedToAttendTrainingObjectiveTruckVehicleWaiter
Attributes 111
Address.textAllergen.typeBanquet.busServiceBanquet.dateBanquet.groupNameBanquet.nameBanquet.numberBanquet.numberPeople
Show all 111 attributes
Address.textAllergen.typeBanquet.busServiceBanquet.dateBanquet.groupNameBanquet.nameBanquet.numberBanquet.numberPeopleBanquet.paymentMethodBanquet.phoneNumberBanquet.timeBusDriver.dateOfBirthBusDriver.driverLicenseNrBusDriver.nameBusDriver.phoneNumberCassette.availableCopiesCassette.titleChef.dateOfBirthChef.nameChef.phoneNumberComment.textCompany.addressCompany.idNumberCompany.nameCompany.poorRiskCompetition.nameCompetition.typeCook.dateOfBirthCook.nameCook.phoneNumberCook.yearsOfExperienceDietaryRequirement.dietFoodItem.descriptionFoodItem.numberFoodItem.purchaseFlagFoodItem.unitFreeRoomTypesDTO.numBedsFreeRoomTypesDTO.numFreeRoomsFreeRoomTypesDTO.pricePerNightFreeRoomTypesDTO.roomTypeDescriptionGeoLocation.latitudeGeoLocation.longitudeHeadWaiter.dateOfBirthHeadWaiter.nameHeadWaiter.phoneNumberIndividual.addressIndividual.dateIndividual.driverLicenseExpirationDateIndividual.driverLicenseNumberIndividual.driverLicenseStateIndividual.homePhoneIndividual.nameIndividual.numberIndividual.numberPeopleIndividual.phoneNumberIndividual.poorRiskIndividual.seatingIndividual.smokingIndividual.timeItemOrder.timeManager.dateOfBirthManager.nameManager.phoneNumberMatchNote.dateMatchNote.noteMatchPlayerPosition.numberMatchPlayerPosition.positionNameMenuItem.classificationMenuItem.descriptionMenuItem.prepTimePerson.emailPerson.namePerson.phonePerson.titlePerson.websitePlayerNotes.datePlayerNotes.noteRegularCustomer.nameRegularCustomer.prefferedLanguageReportedAllergy.allergenSeries.availableCopiesSeries.episodeSeries.titleShipment.idShipment.statusTrainingFailedToAttend.reasonTrainingNotes.dateTrainingObjective.areaToImproveTrainingObjective.endDateTrainingObjective.startDateTrainingObjective.successTruck.expirationDateTruck.gasTankCapacityTruck.idTruck.licensePlateNumberTruck.mileageTruck.odometerReadingTruck.registrationLastMaintenanceDateTruck.registrationStateTruck.vehicleTypeCodeTruck.workingRadioVehicle.expirationDateVehicle.idVehicle.licensePlateNumberVehicle.registrationLastMaintenanceDateVehicle.registrationStateVehicle.vehicleTypeCodeWaiter.dateOfBirthWaiter.nameWaiter.phoneNumberWaiter.spokenLanguage
Relationships 29
AddressContainsGeoLocationBanquetBusDriverBookingBillChefCookCompetitionMatchCustomerConsistsOfShipmentDriverShipmentExpenseComment
Show all 29 relationships
AddressContainsGeoLocationBanquetBusDriverBookingBillChefCookCompetitionMatchCustomerConsistsOfShipmentDriverShipmentExpenseCommentFailedPlayerFoodItemAllergenHeadWaiterWaiterItemOrderMenuItemLocalMatchMatchMatchNoteMatchPlayerMatchPlayerPositionMenuItemChefMenuItemFoodItemPlayerPlayerNotesReservationCustomerReservationItemOrderedReservationWaiterShipmentContainsDeliveryAddressShipmentContainsPickUpAddressStationContainsCustomerStationShipmentTeamTrainingTrainingFaildedTrainingObjectivePlayerVisitorMatch
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)
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 | 4142/∞ |
| Attributes | 10488/10718 |
| Relationships | 4943/∞ |
Quality
Quality Measures the realism of the generated instances. It identifies how much the LLM respects real-world logic. Using Gemini 3.1 Pro as an LLM as a Judge we ask it to rate (realistic, unrealistic, doubtful) the realism of each instance and explain its decision. - Realism = Total Number of "realistic" instances / Total Number of instances
- Judge Cost = (input tokens * input price) + (output tokens * output price)
Quality
Measures the realism of the generated instances. It identifies how much the LLM respects real-world logic. Using Gemini 3.1 Pro as an LLM as a Judge we ask it to rate (realistic, unrealistic, doubtful) the realism of each instance and explain its decision.
- Realism = Total Number of "realistic" instances / Total Number of instances
- Judge Cost = (input tokens * input price) + (output tokens * output price)
| Realism | 56.3% |
| Judge Cost | $6.93 |
Simple - Model Comparison
| Name | Syntax | Mult | Inv | Num.Eq | Str.Eq | Str.LV | Cov.Cls | Cov.Attr | Cov.Rel | Inst.Cls | Inst.Attr | Inst.Rel | Realism |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bank | 100.0% | 100.0% | 100.0% | 99.3% | 100.0% | 92.7% | 100.0% | 100.0% | 100.0% | 240/∞ | 630/630 | 332/∞ | 93.3% |
| Restaurant | 100.0% | 100.0% | 98.3% | 97.8% | 99.8% | 87.3% | 77.0% | 86.6% | 97.4% | 532/∞ | 1756/1756 | 529/∞ | 63.3% |
| AddressBook | 100.0% | 100.0% | 100.0% | — | 100.0% | 89.0% | 100.0% | 100.0% | 100.0% | 291/∞ | 896/896 | 320/∞ | 46.7% |
| PickupNet | 100.0% | 100.0% | 100.0% | 99.2% | 100.0% | 89.2% | 100.0% | 100.0% | 100.0% | 327/∞ | 594/594 | 447/∞ | 60.0% |
| HotelManagement | 100.0% | 100.0% | 100.0% | 98.7% | 99.8% | 77.8% | 85.7% | 81.8% | 100.0% | 202/∞ | 605/605 | 202/∞ | 96.7% |
| Football | 100.0% | 100.0% | 100.0% | 96.2% | 100.0% | 87.4% | 81.3% | 86.5% | 77.8% | 754/∞ | 1838/1838 | 784/∞ | 100.0% |
| MyExpenses | 100.0% | 100.0% | 100.0% | 98.3% | 100.0% | 88.3% | 100.0% | 100.0% | 100.0% | 171/∞ | 573/573 | 189/∞ | 100.0% |
| VideoClub | 100.0% | 100.0% | 100.0% | 96.2% | 100.0% | 83.3% | 80.0% | 78.3% | 100.0% | 235/∞ | 417/417 | 216/∞ | 100.0% |
| VehicleRental | 99.8% | 100.0% | 100.0% | 99.6% | 100.0% | 86.6% | 83.3% | 83.3% | 100.0% | 210/∞ | 1380/1380 | 300/∞ | 33.3% |
| Statemachine | 100.0% | 100.0% | 100.0% | 87.7% | 100.0% | 84.5% | 100.0% | 100.0% | 100.0% | 407/∞ | 407/407 | 659/∞ | 86.7% |
CoT - Model Comparison
| Name | Syntax | Mult | Inv | Num.Eq | Str.Eq | Str.LV | Cov.Cls | Cov.Attr | Cov.Rel | Inst.Cls | Inst.Attr | Inst.Rel | Realism |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bank | 100.0% | 100.0% | 100.0% | 98.5% | 99.9% | 92.1% | 100.0% | 100.0% | 100.0% | 240/∞ | 636/637 | 391/∞ | 100.0% |
| Restaurant | 100.0% | 99.6% | 100.0% | 96.9% | 99.9% | 90.5% | 63.9% | 62.5% | 73.1% | 603/∞ | 1816/2030 | 559/∞ | 60.0% |
| AddressBook | 100.0% | 100.0% | 100.0% | — | 99.9% | 90.1% | 98.9% | 96.9% | 100.0% | 523/∞ | 1506/1520 | 642/∞ | 10.0% |
| PickupNet | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 89.4% | 98.3% | 98.3% | 93.3% | 467/∞ | 773/773 | 550/∞ | 46.7% |
| HotelManagement | 100.0% | 96.5% | 100.0% | 97.6% | 99.6% | 81.2% | 97.1% | 96.4% | 99.4% | 349/∞ | 1052/1052 | 361/∞ | 73.3% |
| Football | 99.8% | 98.5% | 93.8% | 94.8% | 99.9% | 88.8% | 91.5% | 92.2% | 90.4% | 825/∞ | 1933/1934 | 898/∞ | 33.3% |
| MyExpenses | 100.0% | 100.0% | 100.0% | 97.8% | 99.2% | 88.3% | 99.2% | 99.7% | 98.9% | 255/∞ | 778/778 | 277/∞ | 53.3% |
| VideoClub | 100.0% | 100.0% | 100.0% | 94.8% | 100.0% | 83.6% | 82.2% | 81.7% | 100.0% | 281/∞ | 503/503 | 319/∞ | 86.7% |
| VehicleRental | 98.6% | 100.0% | 100.0% | 99.4% | 100.0% | 89.5% | 81.7% | 81.3% | 100.0% | 176/∞ | 1068/1068 | 251/∞ | 23.3% |
| Statemachine | 99.9% | 99.7% | 100.0% | 90.6% | 100.0% | 84.1% | 100.0% | 100.0% | 100.0% | 423/∞ | 423/423 | 695/∞ | 76.7% |