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Prediction of Software Quality Model Using Gene Expression Programming

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 32))

Abstract

There has been number of measurement techniques proposed in the literature. These metrics can be used in assessing quality of software products, thereby controlling costs and schedules. The empirical validation of object-oriented (OO) metrics is essential to ensure their practical relevance in industrial settings. In this paper, we empirically validate OO metrics given by Chidamber and Kemerer for their ability to predict software quality in terms of fault proneness. In order to analyze these metrics we use gene expression programming (GEP). Here, we explore the ability of OO metrics using defect data for open source software. Further, we develop a software quality metric and suggest ways in which software professional may use this metric for process improvement. We conclude that GEP can be used in detecting fault prone classes. We also conclude that the proposed metric may be effectively used by software managers tin predicting faulty classes in earlier phases of software development.

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References

  1. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Empirical Analysis for Investigating the Effect of Object-Oriented Metrics on Fault Proneness: A Replicated Case Study. Software Process Improvement and Practice 14(1), 39–62 (2008)

    Article  Google Scholar 

  2. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Investigating the Effect of Coupling Metrics on Fault Proneness in Object-Oriented Systems. Software Quality Professional 8(4), 4–16 (2006)

    Google Scholar 

  3. Barnett, V., Price, T.: Outliers in Statistical Data. John Wiley & Sons, Chichester (1995)

    Google Scholar 

  4. Basili, V., Briand, L., Melo, W.: A Validation of Object-Oriented Design Metrics as Quality Indicators. IEEE Transactions on Software Engineering 22(10), 751–761 (1996)

    Article  Google Scholar 

  5. Bieman, J., Kang, B.: Cohesion and reuse in an object-oriented system. In: Proceedings of the ACM Symposium on Software Reusability, pp. 259–262 (1995)

    Google Scholar 

  6. Binkley, A., Schach, S.: Validation of the coupling dependency metric as a risk predictor. In: Proceedings of the International Conference on Software Engineering, pp. 452–455 (1998)

    Google Scholar 

  7. Briand, L., Daly, W., Wust, J.: Exploring the relationships between design measures and software quality. Journal of Systems and Software 5, 245–273 (2000)

    Article  Google Scholar 

  8. Briand, L., Wüst, J., Lounis, H.: Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs. Empirical Software Engineering: An International Journal 6(1), 11–58 (2001)

    Article  MATH  Google Scholar 

  9. Cartwright, M., Shepperd, M.: An Empirical Investigation of an Object-Oriented Software System. IEEE Transactions of Software Engineering 26(8), 786–796 (1999)

    Article  Google Scholar 

  10. Chidamber, S., Darcy, D., Kemerer, C.: Managerial use of Metrics for Object-Oriented Software: An Exploratory Analysis. IEEE Transactions on Software Engineering 24(8), 629–639 (1998)

    Article  Google Scholar 

  11. El Emam, K., Benlarbi, S., Goel, N., Rai, S.: A Validation of Object-Oriented Metrics, Technical Report ERB-1063, NRC (1999)

    Google Scholar 

  12. El Emam, K., Benlarbi, S., Goel, N., Rai, S.: The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics. IEEE Transactions on Software Engineering 27(7), 630–650 (2001)

    Article  Google Scholar 

  13. Gyimothy, T., Ferenc, R., Siket, I.: Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Trans. Software Engineering 31(10), 897–910 (2005)

    Article  Google Scholar 

  14. Harrison, R., Counsell, S.J., Nithi, R.V.: An Evaluation of MOOD set of Object-Oriented Software Metrics. IEEE Trans. Software Engineering SE-24(6), 491–496 (1998)

    Article  Google Scholar 

  15. Lee, Y., Liang, B., Wu, S., Wang, F.: Measuring the Coupling and Cohesion of an Object-Oriented program based on Information flow (1995)

    Google Scholar 

  16. Li, W., Henry, S.: Object-Oriented Metrics that Predict Maintainability. Journal of Systems and Software 23(2), 111–122 (1993)

    Article  Google Scholar 

  17. Olague, H., Etzkorn, L., Gholston, S., Quattlebaum, S.: Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes. IEEE Transactions on software Engineering 33(8), 402–419 (2007)

    Article  Google Scholar 

  18. Pai, G.: Empirical analysis of Software Fault Content and Fault Proneness Using Bayesian Methods. IEEE Transactions on software Engineering 33(10), 675–686 (2007)

    Article  Google Scholar 

  19. Tang, M.H., Kao, M.H., Chen, M.H.: An Empirical Study on Object-Oriented Metrics. In: Proceedings of Metrics, pp. 242–249 (1999)

    Google Scholar 

  20. Tegarden, D., Sheetz, S., Monarchi, D.: A software complexity model of object-oriented systems. Decision Support Systems 13(3-4), 241–262 (1995)

    Article  Google Scholar 

  21. Zhou, Y., Leung, H.: Empirical analysis of Object-Oriented Design Metrics for predicting high severity faults. IEEE Transactions on Software Engineering 32(10), 771–784 (2006)

    Article  Google Scholar 

  22. promise, http://promisedata.org/repository/

  23. Moreira, B.C., Fitzjohn, P.W., Offman, M., Smith, G.R., Bates, P.A.: Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence Alignment Space. PROTEINS: Structure, Function, and Genetics 53, 424–429 (2003)

    Article  Google Scholar 

  24. Sheta, A.F.: Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science 2(2), 118–123 (2006)

    Article  Google Scholar 

  25. Tikir, M., Carrington, L., Strohmaier, E., Snavely, A.: A Genetic Algorithms Approach to Modeling the Performance of Memory-bound Computations. In: SC 2007, Reno, Nevada, USA, November 10-16 (2007)

    Google Scholar 

  26. Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13, 87–129 (2001)

    MathSciNet  MATH  Google Scholar 

  27. Sherrod, P.: DTreg Predictive Modeling Software (2003)

    Google Scholar 

  28. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Empirical study of object-oriented metrics. Journal of Object Technology 5(8), 149–173 (2006)

    Article  Google Scholar 

  29. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Software Reuse Metrics for Object-Oriented Systems. In: Third ACIS Int’l Conference on Software Engineering Research, Management and Applications (SERA 2005), pp. 48–55. IEEE Computer Society, Los Alamitos (2005)

    Chapter  Google Scholar 

  30. Briand, L., Daly, W., Wust, J.: Unified Framework for Cohesion Measurement in Object-Oriented Systems. Empirical Software Engineering 3, 65–117 (1998)

    Article  Google Scholar 

  31. Briand, L., Daly, W., Wust, J.: A Unified Framework for Coupling Measurement in Object-Oriented Systems. IEEE Transactions on software Engineering 25, 91–121 (1999)

    Article  Google Scholar 

  32. Chidamber, S., Kemerer, C.: A metrics Suite for Object-Oriented Design. IEEE Trans. Software Engineering SE-20(6), 476–493 (1994)

    Article  Google Scholar 

  33. Henderson-sellers, B.: Object-Oriented Metrics, Measures of Complexity. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  34. Hitz, M., Montazeri, B.: Measuring Coupling and Cohesion in Object-Oriented Systems. In: Proc. Int. Symposium on Applied Corporate Computing, Monterrey, Mexico (1995)

    Google Scholar 

  35. Lake, A., Cook, C.: Use of factor analysis to develop OOP software complexity metrics. In: Proceedings of the 6th Annual Oregon Workshop on Software Metrics, Silver Falls, Oregon (1994)

    Google Scholar 

  36. Lorenz, M., Kidd, J.: Object-Oriented Software Metrics. Prentice-Hall, Englewood Cliffs (1994)

    Google Scholar 

  37. Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359–366 (2000)

    Google Scholar 

  38. jedit, http://sourceforge.net/projects/jedit/

  39. scitools, http://www.scitools.com/index.php

  40. Watanabe, S., Kaiya, H., Kaijiri, K.: Adapting a Fault Prediction Model to Allow Inter Language Reuse. In: PROMISE 2008, Leipzig, Germany, May 12–13 (2008)

    Google Scholar 

  41. Hair, J., Anderson, R., Tatham, W.: Black Multivariate Data Analysis. Pearson Education, London (2000)

    Google Scholar 

  42. Belsley, D., Kuh, E., Welsch, R.: Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons, Chichester (1980)

    Book  MATH  Google Scholar 

  43. Hanley, J., McNeil, B.: The meaning and use of the area under a Receiver Operating Characteristic ROC curve. Radiology 143, 29–36 (1982)

    Article  Google Scholar 

  44. Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc. 36, 111–147 (1974)

    MathSciNet  MATH  Google Scholar 

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Singh, Y., Kaur, A., Malhotra, R. (2009). Prediction of Software Quality Model Using Gene Expression Programming. In: Bomarius, F., Oivo, M., Jaring, P., Abrahamsson, P. (eds) Product-Focused Software Process Improvement. PROFES 2009. Lecture Notes in Business Information Processing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02152-7_5

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  • DOI: https://doi.org/10.1007/978-3-642-02152-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02151-0

  • Online ISBN: 978-3-642-02152-7

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