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Measuring the Quality of Entity Relationship Diagrams

  • Marcela Genero
  • Luis Jiménez
  • Mario Piattini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1920)

Abstract

Database quality depends greatly on the accuracy of the requirement specification and the greatest effort should focus on improving the early stages of database life cycle. Conceptual data models form the basis of all later design work and determine what information can be represented by a database. So, its quality has a significant impact on the quality of the database which is ultimately implemented. In this work, we propose a set of metrics for measuring entity relationship diagram complexity, because in today—s database design world it is still the dominant method of conceptual modelling. The early availability of metrics allows designers to measure the complexity of entityrelationship diagrams in order to improve database quality from the early stages of their life cycle. Also we carried out a controlled experiment in order to analyse the existent relationships between each of the proposed metrics and each of the maintainability sub-characteristics. In order to analyse the obtained empirical data we propose a novel data analysis technique based on fuzzy regression trees.

Keywords

Data Analysis Technique Software Measurement Linguistic Label Theoretical Validation Binary Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Feng, J. The “Information Content” problem of a conceptual data schema and a possible solution. Proceedings of the 4th UKAIS Conference: Information Systems-The Next Generation, University of York, (1999) 257–266Google Scholar
  2. 2.
    Shanks, G. and Darke, P. Quality in Conceptual Modelling: Linking Theory and Practice. Proc. of the Pacific Asia Conference on Information Systems (PACIS’97), Brisbane, (1997) 805–814Google Scholar
  3. 3.
    Muller, R. Database Design For Smarties: Using UML for Data Modeling. Morgan Kaufman, (1999)Google Scholar
  4. 4.
    Moody, D., Shanks, G. and Darke, P. Improving the Quality of Entity Relationship Models — Experience in Research and Practice. Proceedings of the Seventeenth International Conference on Conceptual Modelling (ER’ 98), Singapore, (1998) 255–276Google Scholar
  5. 5.
    Krogstie, J., Lindland, O. and Sindre, G. Towards a Deeper Understanding of Quality in Requirements Engineering, Proceedings of the 7th International Conference on Advanced Information Systems Engineering (CAISE), Jyvaskyla, Finland, June, (1995) 82–95Google Scholar
  6. 6.
    Lindland, O., Sindre, G. and Solvberg, A. Understanding Quality in Conceptual Modelling. IEEE Software, March, Vol. 11 N‡ 2, (1994) 42–49Google Scholar
  7. 7.
    Schuette, R. and Rotthowe, T. The Guidelines of Modeling — An Approach to Enhance the Quality in Information Models. Proceedings of the Seventeenth International Conference on Conceptual Modelling (ER’ 98), Singapore, November 16–19, (1998) 40–254Google Scholar
  8. 8.
    Eick, C. A Methodology for the Design and Transformation of Conceptual Schemas. Proc. of the 17th International Conference on Very Large Data Bases. Barcelona (1991)Google Scholar
  9. 9.
    Gray, R., Carey, B., McGlynn, N. and Pengelly A. Design metrics for database systems. BT Technology, Vol. 9 N‡ 4, (1991) 69–79Google Scholar
  10. 10.
    Moody, D. Metrics for Evaluating the Quality of Entity Relationship Models. Proceedings of the Seventeenth International Conference on Conceptual Modelling (ER’ 98), Singapore, November 16–19, (1998) 213–225Google Scholar
  11. 11.
    Kesh, S. Evaluating the Quality of Entity Relationship Models. Information and Software Technology, Vol. 37 N‡ 12, (1995) 681–689.CrossRefGoogle Scholar
  12. 12.
    ISO/IEC 9126-1. Information technology-Software product quality — Part 1: Quality model. (1999)Google Scholar
  13. 13.
    Batini, C., Ceri, S. and Navathe, S. Conceptual database design. An entity relationship approach. Benjamin Cummings Publishing Company. (1992)Google Scholar
  14. 14.
    Li, H. and Cheng, W. An empirical study of software metrics. IEEE Transactions on Software Engineering, Vol. 13 N‡ 6, (1987) 679–708CrossRefGoogle Scholar
  15. 15.
    Fenton, N. and Pfleeger, S. Software Metrics: A Rigorous Approach. 2nd. edition. Chapman & Hall, London (1997)Google Scholar
  16. 16.
    Kitchenham, B., Pflegger, S. and Fenton, N. Towards a Framework for Software Measurement Validation. IEEE Transactions of Software Engineering, Vol. 21 N‡ 12, (1995) 929–943CrossRefGoogle Scholar
  17. 17.
    Schneidewind, N. Methodology For Validating Software Metrics. IEEE Transactions of Software Engineering, Vol. 18 N‡ 5, (1992) 410–422CrossRefGoogle Scholar
  18. 18.
    Basili, V., Shull, F. and Lanubile, F. Building knowledge through families of experiments. IEEE Transactions on Software Engineering, Vol. 25 N‡ 4, (1999) 435–437CrossRefGoogle Scholar
  19. 19.
    Briand, L., Morasca, S. and Basili, V.. Property-Based Software Engineering Measurement. IEEE Transactions on Software Engineering, Vol. 22 N‡ 6, (1996) 68–86CrossRefGoogle Scholar
  20. 20.
    Genero, M., Piattini, M. and Calero, C. (2000). Formalization of Metrics for Conceptual Data Models. UKAIS 2000. Cardiff, 26–28 April, (2000) 99–119Google Scholar
  21. 21.
    Zuse, H. A Framework of Software Measurement. Walter de Gruyter, Berlin (1998)Google Scholar
  22. 22.
    Genero, M., Piattini, M., Calero, C. Serrano, M. (2000). Measures to get better quality databases. ICEIS 2000. Stafford, 4–7 July, (2000) 49–55Google Scholar
  23. 23.
    Fenton, N. Software Measurement: A Necessary Scientific Basis. IEEE Transactions on Software Engineering, Vol. 20 N‡ 3, (1994) 199–206CrossRefGoogle Scholar
  24. 24.
    Henderson-Sellers, B. Object-oriented Metrics-Measures of complexity. Prentice-Hall, Upper Saddle River, New Jersey. (1996)Google Scholar
  25. 25.
    Lethbridge, T. Metrics For Concept-Oriented Knowledge bases. International Journal of Software Engineering and Knowledge Engineering Vol. 8 N‡ 2, (1998) 161–188CrossRefGoogle Scholar
  26. 26.
    Ruiz, I. and Gómez-Nieto, M. Diseño y uso de Bases de Datos Relacionales. RaMa, (1997) (in spanish)Google Scholar
  27. 27.
    De Miguel, A. and Piattini, M. Fundamentos y Modelos de Bases de Datos. RaMa. (1997) (in spanish)Google Scholar
  28. 28.
    Calero, C., Pascual, C., Serrano, M. and Piattini, M. Measuring Oracle Database Schema. Computers and Computational Engineering in Control, (Cap. 42), World Scientific Engineering Society, (1999) 237–243Google Scholar
  29. 29.
    Morasca, S. and Ruhe, G. Guest Editors’Introduction: Knowledge Discovery From Empirical Software Engineering Data. International Journal of Software Engineering and Knowledge Engineering, Vol. 9 N‡ 5, (1999) 495–498CrossRefGoogle Scholar
  30. 30.
    Zadeh, L. The Concept of Linguistic Variable and its Applications to Approximate Reasoning Part I. Information Sciences, Vol. 8, (1973) 199–249.CrossRefMathSciNetGoogle Scholar
  31. 31.
    Breiman, L., Friedman, J., Olshen, R. and Stone, C.. Classification and Regression Trees. Wadsworth, Belmont, CA, (1984)Google Scholar
  32. 32.
    Linares, L., Delgado, M. and Skarmeta, A. Regression by fuzzy knowledge bases. Proceedings of the 4th European Congress on Intelligent Techniques and soft computing. Aachen, Germany, September, (1996) 1170–1176Google Scholar
  33. 33.
    Zadeh, L. Fuzzy sets. Information and control, (1965) 338–353.Google Scholar
  34. 34.
    Sugeno, M. An Introductory Survey of Fuzzy Control. Information Sciences, Vol. 36, (1985) 59–83.zbMATHCrossRefMathSciNetGoogle Scholar
  35. 35.
    Rumbaugh, J., Blaha M., Premerlani, W., Eddy, F., and Lorensen, W. Object-Oriented Modeling and Design. Prentice Hall, USA, (1991)Google Scholar
  36. 36.
    Genero, M., Manso, Ma E., Piattini, M. and García, F. Assessing the Quality and the Complexity of OMT Models. 2nd European Software Measurement Conference-FESMA 99, Amsterdam, The Netherlands, (1999) 99–109Google Scholar
  37. 37.
    Booch, G., Rumbaugh, J. and Jacobson, I. The Unified Modeling Language User Guide. Addison-Wesley, (1998)Google Scholar
  38. 38.
    Genero, M., Piattini, M. and Calero, C. Métricas para Jerarquías de Agregación en diagramas de clases UML. Memorias del Jornadas Iberoamericanas de Ingeniería de Requisitos y ambientes de Software, IDEAS’2000, Cancún, México, 5–7 Abril, (2000) 373–384 (in spanish)Google Scholar
  39. 39.
    Poels, G. On the use of a Segmentally Additive Proximity Structure to Measure Object Class Life Cycle Complexity. Software Measurement: Current Trends in Research and Practice. Deutscher Universitäts Verlag, (1999) 61–79Google Scholar
  40. 40.
    Poels, G. On the Measurement of Event-Based Object-Oriented Conceptual Models. 4th International ECOOP Workshop on Quantitative Approaches in Object-Oriented Software Engineering, June 13, Cannes, France. (2000)Google Scholar
  41. 41.
    Brito e Abreu, F., Zuse, H., Sahraoui, H. and Melo, W. Quantitative Approaches in Object-Oriented Software Engineering. Object-Oriented technology: ECOOP’99 Workshop Reader, Lecture Notes in Computer Science 1743, Springer-Verlag, (1999) 326–337.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marcela Genero
    • 1
  • Luis Jiménez
    • 2
  • Mario Piattini
    • 1
  1. 1.Grupo ALARCOSCiudad Real (Spain)
  2. 2.Grupo ORETOUniversity of Castilla-La ManchaCiudad Real (Spain)

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