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A Fuzzy Model and the AdeQuaS Fuzzy Tool: a theoretical and a practical view of the Software Quality Evaluation

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Software Engineering with Computational Intelligence

Abstract

This work introduces the Fuzzy Model for Software Quality Evaluation and its implementation the AdeQuaS Fuzzy tool. The model proposed here comprises a five-stage evaluation process, and it may involve three distinct situations. In the first situation, the evaluation objective is to establish a quality standard for the software product or application domain in question. In the second one, the quality evaluation of a software product is executed, based upon a pre-defined quality standard. In the third, a quality estimation of a software product is found when there is not quality standard available. The AdeQuaS Fuzzy tool, which is based on the Fuzzy Model, has the objective of supporting the stages of software evaluation process, in order to get more effective results about the quality degree of subjective attributes through the judgment of a group of specialists. Besides, it is presented two applications. The first is the evaluation process to e-commerce websites quality. The second is an evaluation of software requirements specifications quality.

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References

  • Albuquerque, A. B. (2001), “Electronic Commerce Websites Quality”. MSc Thesis, Department of Computer Science, University of Fortaleza, Fortaleze, CE. (in Portuguese).

    Google Scholar 

  • Araújo, K. (2000), “Fuzzy Logic: history, concepts and fuzzy model applications”, Developers Magazine, April, 28–33 (in Portuguese).

    Google Scholar 

  • Baldwin, J. F. (1979), “A new approach to approximate reasoning using a fuzzy logic”, Fuzzy Sets and Systems 2, 309–325.

    Article  MathSciNet  MATH  Google Scholar 

  • Bardossy, A., Duckstein. L. and Bogardi, I. (1993), “Combination of fuzzy number representing expert opinions”, Fuzzy Sets and Systems 57, 173–181.

    Article  MathSciNet  Google Scholar 

  • Belchior, A. D. (1997), “A Fuzzy Model for Software Quality Evaluation”, DSc Thesis, Department of Systems Engineering and Computer Science, Federal University of Rio de Janeiro, RJ (in Portuguese).

    Google Scholar 

  • Boehm, B. and Hoh, I. (1996), “Identifying Quality Requirements Conflicts”, IEEE Software, 25–35.

    Google Scholar 

  • Boloix, G. et.al. (1995), “A software system evaluation framework”, IEEE Software, 17–26.

    Google Scholar 

  • Branco Jr., E. C., Belchior, A. C. (2001), “Management processes of software projects: a qualitative approach”, In VIII Software Quality Workshop, Rio de Janeiro, RJ.

    Google Scholar 

  • Campos, F. et al. (1998), “Farming software quality: a user’s view”, IX International Conference of Software Technology, Curitiba, PR (in Portuguese).

    Google Scholar 

  • Chen, C. T., Hsy, H. M. (1993), A study of fuzzy TOPSIS model, Proc. of the Chinese Institute of Industrial Engineers National Conference, in (Hsu, 1996).

    Google Scholar 

  • Clunie, C. E. (1997) Quality Evaluation of Object-Oriented Specifications. DSc. Thesis, Department of Systems Engineering and Computer Science, Federal University of Rio de Janeiro, RJ (in Portuguese).

    Google Scholar 

  • Dubois, D. and Prade, H. (1980), Fuzzy Sets and Systems: Theory and Applications, Academic Press, NY.

    MATH  Google Scholar 

  • Dubois, D. and Prade, H. (1991), “Fuzzy sets in approximate reasoning. Part 1: Inference with possibility distributions”, Fuzzy Sets and Systems 40, IFSA, Special Memorial Volume: 25 years of fuzzy sets, North-Holland, Amsterdam, 143–202.

    Google Scholar 

  • Dyer, M. (1992), The cleanroom approach to Quality Software Development, John Wiley & Sons, Inc. NY.

    MATH  Google Scholar 

  • Fenton, N. E. and Ptleeger, S. L. (1997), Software Metrics: a rigorous & practical approach, 2 Edition, PWS Publishing Company, Boston, MA.

    Google Scholar 

  • Idri, A. et.al. (2000), “COCOMO — Cost model using fuzzy logic”, In 7th International Conference on Fuzzy Theory & Technology. Atlantic City, NJ.

    Google Scholar 

  • Gray, A. (1997), “Applications of fuzzy logic to software metric models for development effort estimation”, In Proceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE Computer Society Press, Syracuse, NY, pp. 394–399.

    Google Scholar 

  • Hapke, M. et.al. (1994), “Fuzzy project scheduling system for software development”, Fuzzy Sets and Systems 67, 101–117.

    Article  MathSciNet  Google Scholar 

  • Hsu, H. M. and Chen, C. T (1996), “Aggregation of fuzzy opinions under group decision making”, Fuzzy Sets and Systems 79, 279–285.

    Article  MathSciNet  Google Scholar 

  • Ibrahim, A. and Ayyub, B. M. (1992), “Multi-criteria ranking of components according to their priority for inspection”, Fuzzy Sets and Systems 48, 1–14.

    Article  MATH  Google Scholar 

  • ISO (2001), ISO/IEC 9126-1, “Software engineering — Product quality — Part 1: quality model”.

    Google Scholar 

  • ISO (1998), ISO/IEC 14598-1, “Information technology — software product evaluation — Part 1: general overview”.

    Google Scholar 

  • Ishikawa, A. et.al, (1993), “The max-min Delphi method and fuzzy Delphi method via fuzzy integration”, Fuzzy Sets and Systems 55, 241–253.

    Article  Google Scholar 

  • Kacprzyk, J. et.al. (1992), “Group decision making and consensus under fuzzy preference and fuzzy majority”, Fuzzy Sets and Systems 49, 21–31.

    Article  MathSciNet  MATH  Google Scholar 

  • Kaufmann, A. and Gupta, M. M. (1991), Introduction to Fuzzy Arithmetic: theory and applications. Van Nostrand Reinhold, NY.

    MATH  Google Scholar 

  • Kitchenham, B. et.al. (1996), “Software Quality: the elusive target”, IEEE Software, 12–21.

    Google Scholar 

  • Lasek, M. (1992), “Hierarchical structures of fuzzy ratings in the analysis of strategic goal of enterprises”, Fuzzy Sets and Systems 50, 127–134.

    Article  MATH  Google Scholar 

  • Lee, H. M. (1996a), “Applying fuzzy set theory to evaluate the rate of aggregative risk in software development”, Fuzzy Sets and Systems 79, 323–336.

    Article  Google Scholar 

  • Lee, H. M. (1996b), “Group decision making using fuzzy theory for evaluating the rate of aggregative risk in software development”, Fuzzy Sets and Systems 80, 261–271.

    Article  Google Scholar 

  • Lima Jr. O. S. et.al. (2001), “Maintenance project assessments using fuzzy function point analysis”, Seventh Workshop on Empirical Studies of Software Maintenance, IEEE Computer Society, Florence, Italy, 114–121.

    Google Scholar 

  • Liou, T. S., Jiun, M. and WG, J. (1992), “Fuzzy weighted average: an improved Algorithm”, Fuzzy Sets and Systems 49,307–315.

    Article  MathSciNet  MATH  Google Scholar 

  • Möller, K. H. (1993), Software metrics: a practitioner’s guide to improved product development, Chapman & Hall Computing, London, England.

    Google Scholar 

  • Palermo, S. and Rocha, A. R. C (1989), “An experience on evaluating software quality for high energy physics”, Computer Physics Communications.

    Google Scholar 

  • Pedrycz, W. and Peters, J. F. (1998) “Software Quality Measurement: concepts and fuzzy neural relational model”, http://neuron.et.ntust.edu.tw/homework/89/FL/89homework/M8709022/3.pdf./homework/89/FL/89homework/M8709022/3.pdf.

    Google Scholar 

  • Pfleeger, S. L. (1998) Software Engineering: theory and practice, Prentice Hall, NJ.

    Google Scholar 

  • Pressman, R. S. (2000), Software engineering: a practitioner’s approach. Fifth Edition. McGraw Hill, NY.

    Google Scholar 

  • Ribeiro, R. A. (1996), “Fuzzy multiple attribute decision making: a review and new preference elicitation techniques”, Fuzzy Sets and Systems 78, 155–181.

    Article  MathSciNet  MATH  Google Scholar 

  • Rocha, A. R. C. (1983), “A model for specification quality evaluation”, D.Sc. Thesis, Department of Systems Engineering and Computer Science, Pontifical Catholic University (PUC), Rio de Janeiro, RJ (in Portuguese).

    Google Scholar 

  • Rocha, A. R. C. et al. (2001), Software Quality: theory and practice, Prentice Hall, Sao Paulo, Brazil. (in Portuguese).

    Google Scholar 

  • Römer, C. and Kandel, A. (1995), “Statistical tests for fuzzy data”, Fuzzy Sets and Systems 72, 1–26.

    Article  MathSciNet  MATH  Google Scholar 

  • Ruoning, X. and Xiaoyan, Z. (1992), “Extensions of the analytic hierarchy process in fuzzy environment”, Fuzzy Sets and Systems 52, 251–257.

    Article  MathSciNet  Google Scholar 

  • Ryder, J. (1998) “Fuzzy modeling of software effort prediction.”, IEEE Information Technology Conference, Syracuse, NY, pp 53–56.

    Google Scholar 

  • Schneidewind, N. F. (1992), “Methodology for validating software metrics”, IEEE Transaction Software Engineering, vol. 18, n° 5, May, 1992, in (Fenton, 1994).

    Google Scholar 

  • Simão, R. P. S. (2002), “A quality standard to software components”, In I Brazilian Symposium of Software Quality, Gramado, RS, 249–260.

    Google Scholar 

  • Simonelli, M. R. (1996), “On fuzzy interactive knowledge”, Fuzzy Sets and Systems 80, 159–165.

    Article  MathSciNet  MATH  Google Scholar 

  • Sousa, C. P. (1995) “Fuzzy Logic Introduction”. http://dee.ufe.br/~pimentel/ica/ica.html/~pimentel/ica/ica.html. (in Portuguese).

    Google Scholar 

  • Zadeh, L. A. (1988), “Fuzzy Logic”, IEEE Transaction Computer, vol. 21, 83–93.

    Google Scholar 

  • Zimmermann, H. J. (1996), Fuzzy Set Theory and Its Applications, Third Edition, Kluwer Academic Publishers, Boston, MA.

    MATH  Google Scholar 

  • Zwick, R., Edward Carlstein and David V. Budescu (1987), “Measures of similarity among fuzzy concepts: A comparative analysis,” International Journal of Approximate Reasoning 1. 221–242.

    Article  MathSciNet  Google Scholar 

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Oliveira, K.R., Belchior, A.D. (2003). A Fuzzy Model and the AdeQuaS Fuzzy Tool: a theoretical and a practical view of the Software Quality Evaluation. In: Khoshgoftaar, T.M. (eds) Software Engineering with Computational Intelligence. The Springer International Series in Engineering and Computer Science, vol 731. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0429-0_5

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  • DOI: https://doi.org/10.1007/978-1-4615-0429-0_5

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