Skip to main content

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

  • Conference paper

Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

  • Data Mining
  • Fuzzy Clustering
  • Software Engineering
  • Random forest
  • Metrics
  • Software Quality
  • Project Management

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-03095-1_34
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-03095-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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)

    CrossRef  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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. Ramaswamy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

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

Download citation

  • 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)