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Position Paper: Defect Prediction Approaches for Software Projects Using Genetic Fuzzy Data Mining

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ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 249))

Abstract

Despite significant advances in software engineering research, the ability to produce reliable software products for a variety of critical applications remains an open problem. The key challenge has been the fact that each software product is unique, and existing methods are predominantly not capable of adapting to the observations made during project development. This paper makes the following claim: Genetic fuzzy data mining methods provide an ideal research paradigm for achieving reliable and efficient software defect pattern analysis. A brief outline of some fuzzy data mining methods is provided, along with a justification of why they are applicable to software defect analysis. Furthermore, some practical challenges to the extensive use of fuzzy data mining methods are discussed, along with possible solutions to these challenges.

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References

  1. Gallaher, M., Kropp, B.: Economic impacts of inadequate infrastructure for software testing. Technical report, National Institute of Standards and Technology (May 2002)

    Google Scholar 

  2. Bohem, B.W., Sullivan, K.: Software economics: A roadmap. In: International Conference on Software Engineering, Limerick, Ireland, pp. 319–343 (2000)

    Google Scholar 

  3. Bertolino, A., Strigini, L.: On the Use of Testability Measures for Dependability Assessment. IEEE Trans. Software Engineering 22(2), 97–108 (1996)

    Article  Google Scholar 

  4. Bishop, M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)

    Google Scholar 

  5. Boetticher, G.D., Srinivas, K., Eichmann, D.: A Neural Net-Based Approach to Software Metrics. In: Proceedings of the Fifth International Conference on Software Engineering and Knowledge Engineering, San Francisco, pp. 271–274 (1993)

    Google Scholar 

  6. CHAOS Chronicles, The Standish Group - Standish Group Internal Report (1995)

    Google Scholar 

  7. Cusumano, M.A.: Japan’s Software Factories. Oxford University Press (1991)

    Google Scholar 

  8. Diaz, M., Sligo, J.: How Software Process Improvement Helped Motorola. IEEE Software 14(5), 75–81 (1997)

    Article  Google Scholar 

  9. Dickinson, W., Leon, D., Podgurski, A.: Finding failures by cluster analysis of execution profiles. In: ICSE, pp. 339–348 (2001)

    Google Scholar 

  10. Fenton, N., Neil, M.: A critique of software defect prediction models. IEEE Transactions on Software Engineering 25(5), 675–689 (1999)

    Article  Google Scholar 

  11. Groce, A., Visser, W.: What went wrong: Explaining counterexamples. In: Ball, T., Rajamani, S.K. (eds.) SPIN 2003. LNCS, vol. 2648, pp. 121–135. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Jensen, F.V.: An Introduction to Bayesian Networks. Springer (1996); Henry, S., Kafura, D.: The Evaluation of Software System’s Structure Using Quantitative Software Metrics. Software Practice and Experience 14(6), 561–573 (1984)

    Google Scholar 

  13. Hudepohl, P., Khoshgoftaar, M., Mayrand, J.: Integrating Metrics and Models for Software Risk Assessment. In: The Seventh International Symposium on Software Reliability Engineering (ISSRE 1996) (1996)

    Google Scholar 

  14. Mitchell, T.M.: Machine Learning. McGrawHill (1997); Neumann, D.E.: An Enhanced Neural Network Technique for Software Risk Analysis. IEEE Transactions on Software Engineering, 904–912 (2002)

    Google Scholar 

  15. Yuriy, B., Ernst, M.D.: Finding latent code errors via machine learning over program executions. In: Proceedings of the 26th International Conference on Software Engineering, Edinburgh, Scotland (2004)

    Google Scholar 

  16. Beecham, S., Hall, T., Bowes, D., Gray, D., Counsel, S., Black, S.: A Systematic Review of Fault Prediction Approaches used in Software Engineering

    Google Scholar 

  17. Shan, X., Jiang, G., Huang, T.: A framework of estimating software project success potential based on association rule mining. IEEE, doi:978-1-4244-4639-1/09/$25.00 ©2009

    Google Scholar 

  18. Pinto, J.K., Slevin, D.P.: Project success: definitions and measurement techniques. Project Management Journal 19, 67–72 (1988)

    Google Scholar 

  19. Jones, C.: Patterns of large software systems: failure and success. IEEE Computer 28, 86–87 (1995)

    Google Scholar 

  20. Baccarini, D.: The logical framework method for defining project success. Project Management Journal 30, 25–32 (1999)

    Google Scholar 

  21. Linberg, K.R.: Software developer perceptions about software project failure: a case study. The Journal of Systems and Software 49, 177–192 (1999)

    Article  Google Scholar 

  22. Weka 3: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/

  23. Zhang, H., Kitchenham, B., Jeffery, R.: Achieving Software Project Success: A Semi-quantitative Approach. In: Wang, Q., Pfahl, D., Raffo, D.M. (eds.) ICSP 2007. LNCS, vol. 4470, pp. 332–343. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  24. Martin, N.L., Pearson, J.M., Furumo, K.A.: IS Project Management: Size, Complexity, Practices and the Project Management Office. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS 2005) - Track 8, p. 234b (2005)

    Google Scholar 

  25. Weber, R., Waller, M., Verner, J.M., Evanco, W.M.: Predicting Software Development Project Outcomes. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 595–609. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  26. King, M.A., Elder IV, J.F.: Evaluation of fourteen desktop data mining tools. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 27–29 (1998)

    Google Scholar 

  27. Diehl, S., Gall, H., Hassan, A.E.: Guest editors introduction: special issue on mining software repositories. Empirical Software Engineering 14(3), 257–261 (2009)

    Article  Google Scholar 

  28. Mendonca, M., Sunderhaft, N.L.: Mining software engineering data: A survey. A DACS state-of-the-art report, Data & Analysis Center for Software, Rome, NY (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of CSE, BIET, Davanagere, India

    V. Ramaswamy

  2. RIIC, Dayanada Sagar Institute, Jain University, Bangalore, Bangalore, India

    T. P. Pushphavathi

  3. Dept. of CSE, RIIC, Dayanada Sagar Institute, Bangalore, India

    V. Suma

Authors
  1. V. Ramaswamy
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  2. T. P. Pushphavathi
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  3. V. Suma
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Corresponding author

Correspondence to V. Ramaswamy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. College of Engineering(A), Andhra University, Vishakapatnam, India

    P. S. Avadhani

  3. University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  4. CSIR-National Institute of Oceanography, Visakhapatnam, India

    Sadasivuni Lakshminarayana

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Ramaswamy, V., Pushphavathi, T.P., Suma, V. (2014). Position Paper: Defect Prediction Approaches for Software Projects Using Genetic Fuzzy Data Mining. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II. Advances in Intelligent Systems and Computing, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-319-03095-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-03095-1_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03094-4

  • Online ISBN: 978-3-319-03095-1

  • eBook Packages: EngineeringEngineering (R0)

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