Measuring UML Conceptual Modeling Quality

Samira SI-SAID CHERFI*Jacky AKOKA* Isabelle COMYN-WATTIAU*

* CEDRIC - CNAM – Paris (France)

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Outline

Introduction Related literature Contribution

Quality framework Quality metrics Quality assessment validation

Conclusion Future work

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Motivations

Focus on early artifacts (conceptual schemas) Quality assessment approach

Quality criteria Quality metrics

Purpose-oriented quality assessment Weighted quality metrics

Extensible quality metrics Genericity of metrics

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Related literature

Software products quality Fault prediction through program code

quality assessment [Davis, 90],...

Data quality Intuitive [Wang,95],... Theoretical [Wand,96], ... Empirical [Wang,96], ...

Conceptual schemas quality [Batini,92],... Mainly qualitative criteria

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Conceptual quality assessment

A conceptual schema should:

Provide a formal representation of the

observed reality

Meet the users requirements

Be a basis for the future IS

implementation and evolution

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Quality assessment approach Quality criteria measured by quality

metrics along each dimensionMotivations

Related literature

Contribution

Quality

framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

Quality ImplementationImplementabilityMaintainability

SpecificationLegibility

ClarityMinimality

ExpressivenessSimplicityCorrectness

1

1 1

UsageCompletenessUnderstandability

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Quality assessment approach The specification dimension: efficiently

use the notation provided by the model

Easy to read (visual considerations)

Simple (nature of concepts)

Expressive enough (richness of models)

Correct

Legibility

Simplicity

Expressiveness

Correctness

Motivations

Related literature

Contribution

Quality

framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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The usage dimension: how close is the conceptual schema to the users perception

Quality assessment approach

Complete (requirements coverage)

Easy to understand (how close are the modeling concepts to the users reality)

Completeness

Understandability

Motivations

Related literature

Contribution

Quality

framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Quality assessment approach The implementation dimension: ease of

models implementation and evolution

How far are the conceptual constructs from the implementation aspects?

How easy is the evolution of the information system?

Implementability

Maintainability

Motivations

Related literature

Contribution

Quality

framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Metrics evaluation: Legibility (Clarity)

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

0.33 1

)()()()()()(HNBACNBANB

CRHNBACNBANB

City

Employee

1..1

0..*

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0..*

lives_in

1..1

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1..1

0..*works_in

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+subordinate_to

1..1+manager_

0..*

City

Employee

1..1

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lives_in

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works_in

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+manage

1..1

+subordinate 0..*

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redundancy)

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

0.66 1

Administration_staff

Medical_staff

Service

1..*

1..*

1..*

1..*

works_in

1..*

1..*

1..*works_in Administration_staff Medical_staff

ServiceStaff 1..* 1..*1..* 1..*

works_in

S

SR

CiNBwi

CiNBwiCiNBwi

)(

)()(

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Metrics evaluation: Legibility (Aggregation degree)

)()((

11

CiNBCiUnnestLevel

ci

Where Level(Unnest(Ci)) counts the number of aggregation levels of an attribute Ci and NB(Ci) is the number of attributes in the schema

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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)()()()(

1 HNBCiNBCiUSECiDEF

H Ci

Where H is a hierarchy, Ci Є {attribute, association, operation}, DEF(Ci) counts the number of occurrences of Ci in H, USE(Ci) counts the number of inheritances of an element Ci+1, NB(H) is the number of hierarchies, and NB(Ci) the number concepts in H

Metrics evaluation: Legibility (Factorization degree)

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Metrics evaluation: Expressiveness

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Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

S

jj

Sii

UCNBw

CNBw

)(

)(

Doctor

Practitioner

Researcher

Doctor

Practitioner

Researcher

Independent_consultant

Practitioner_researcher

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Metrics evaluation: Simplicity

0.6 0.5

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

)()()()()()(

ACNBCNBHNBANBACNBCNB

Doctor

Practitioner

Researcher

Doctor

Practitioner

Researcher

Independent_consultant

Practitioner_researcher

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Quality assessment

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality

assessment

validation

Conclusion

Future work

Genericity of metrics Application to Entity-relationship

formalism[ER2002]

Application to a case study The quality of a schema depends on its purpose

Constructing a « good » conceptual schema implies

making a compromise between several quality

criteria

The difficulty of defining and interpreting a quality

absolute value

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Quality assessment

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Quality assessment: prototype

XML interface

Quali ty module

An existing CASE tool

Adding quality measurement functionality to a CASE tool

Construct an evolutive and modular solution

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Quality assessment: prototype

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Conclusion

A quality evaluation framework

Quality criteria and quality metrics

Purpose-orientation of the metrics

Genericity of metrics

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Future work

Enrich the framework (implementation

and usage)

Metrics for dynamic aspects in O. O.

approaches

Use the framework for quality

approaches comparison

Motivations

Related literature

Contribution

Quality framework

Quality metrics

Quality assessment

validation

Conclusion

Future work

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Questions