Measuring UML Conceptual Modeling Quality Samira SI-SAID CHERFI* Jacky AKOKA* Isabelle COMYN-WATTIAU* * CEDRIC - CNAM – Paris (France)

BDA 2002 october21th-25th 2 Outline Introduction Related literature Contribution Quality framework Quality metrics Quality assessment validation Conclusion Future work

BDA 2002 october21th-25th 3 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

BDA 2002 october21th-25th 4 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

BDA 2002 october21th-25th 5 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

BDA 2002 october21th-25th 6 Quality assessment approach Quality criteria measured by quality metrics along each dimension  Motivations  Related literature  Contribution  Quality framework Quality metrics Quality assessment validation  Conclusion  Future work Quality Implementation Implementability Maintainability Specification Legibility Clarity Minimality Expressiveness Simplicity Correctness Usage Completeness Understandability

BDA 2002 october21th-25th 7 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

BDA 2002 october21th-25th 8 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

BDA 2002 october21th-25th 9 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

BDA 2002 october21th-25th 10 Metrics evaluation: Legibility ( Clarity )  Motivations  Related literature  Contribution  Quality framework  Quality metrics  Quality assessment validation  Conclusion  Future work 0.331

BDA 2002 october21th-25th 11 Metrics evaluation: Legibility ( Non- redundancy )  Motivations  Related literature  Contribution  Quality framework  Quality metrics  Quality assessment validation  Conclusion  Future work Administration_staff Medical_staff Service 1..* works_in 1..* works_in Administration_staffMedical_staff Service Staff 1..* works_in

BDA 2002 october21th-25th 12 Metrics evaluation: Legibility ( Aggregation degree) 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

BDA 2002 october21th-25th 13 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

BDA 2002 october21th-25th 14 Metrics evaluation: Expressiveness  Motivations  Related literature  Contribution  Quality framework  Quality metrics  Quality assessment validation  Conclusion  Future work Doctor Practitioner Researcher Independent_consultant Practitioner_researcher

BDA 2002 october21th-25th 15 Metrics evaluation: Simplicity  Motivations  Related literature  Contribution  Quality framework  Quality metrics  Quality assessment validation  Conclusion  Future work Doctor Practitioner Researcher Independent_consultant Practitioner_researcher

BDA 2002 october21th-25th 16 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

BDA 2002 october21th-25th 17 Quality assessment

BDA 2002 october21th-25th 18 Quality assessment: prototype Adding quality measurement functionality to a CASE tool Construct an evolutive and modular solution

BDA 2002 october21th-25th 19 Quality assessment: prototype

BDA 2002 october21th-25th 20 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

BDA 2002 october21th-25th 21 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

BDA 2002 october21th-25th 22 Questions