Early Fault PredictionÔ Using Software Metrics and Process Maturity

  • Ajeet Kumar Pandey
  • Neeraj Kumar Goyal
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 303)


Development of reliable software is challenging as system engineers have to deal with a large number of conflicting requirements such as cost, time, reliability, safety, maintainability, and many more. These days, most of the software development tasks are performed in labor-intensive way. This may introduce various faults across the development, causing failures in the near future. The impact of these failures ranges from marginal to catastrophic consequences. Therefore, there is a growing need to ensure the reliability of these software systems as early as possible. A model for early prediction of software fault is presented in this chapter.


Fuzzy Inference System Process Maturity Software Reliability Fuzzy Logic System Fault Prediction 
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.


  1. IEEE (1991). IEEE standard glossary of software engineering terminology. ANSI/IEEE, STD-729–991.Google Scholar
  2. Agrawal, M., & Chari, K. (2007). Software effort, quality and cycle time: A study of CMM level 5 projects. IEEE Transaction on Software Engineering, 33(3), 145–156.CrossRefGoogle Scholar
  3. Musa, J. D., Iannino, A., & Okumoto, K. (1987). Software reliability: Measurement, prediction, and application. New York: McGraw–Hill Publication.Google Scholar
  4. Kaner, C. (2004). Software engineering metrics: What do they measure and how do we know? In 10th International Software Metrics Symposium, METRICS.Google Scholar
  5. Pham, H. (2006). System software reliability, reliability engineering series. London: Springer.Google Scholar
  6. Zadeh, L. A. (1965). Fuzzy sets, information and control (Vol. 8(3) pp. 338–353).Google Scholar
  7. Ross, T. J. (2005). Fuzzy logic with engineering applications (2nd ed.). India: Willey.Google Scholar
  8. Gaffney, G. E., & Pietrolewiez, J. (1990). An automated model for software early error prediction (SWEEP). In Proceeding of 13th Minnow Brook Workshop on Software Reliability.Google Scholar
  9. Fenton, N. (1991). Software metrics: A rigorous approach. London: Chapmann & Hall.MATHGoogle Scholar
  10. Zhang, X., & Pham, H. (2000). An analysis of factors affecting software reliability. The Journal of Systems and Software, 50(1), 43–56.CrossRefGoogle Scholar
  11. Li, M., & Smidts, C. (2003). A ranking of software engineering measures based on expert opinion. IEEE Transaction on Software Engineering, 29(9), 811–824.CrossRefGoogle Scholar
  12. Paulk, M. C., Weber, C. V., Curtis, B., & Chrissis, M. B. (1993). Capability maturity model version 1.1. IEEE Software, 10(3), 18–27.CrossRefGoogle Scholar
  13. Diaz, M., & Sligo, J. (1997). How software process improvement helped Motorola. IEEE Software, 14(5), 75–81.CrossRefGoogle Scholar
  14. IEEE (1988). IEEE guide for the use of ieee standard dictionary of measures to produce reliable software. IEEE Standard 982.2.Google Scholar
  15. NASA (2004). NASA metrics data program.
  16. Kumar, K. S., & Misra, R. B. (2008). An enhanced model for early software reliability prediction using software engineering metrics. In Proceedings of 2nd International Conference on Secure System Integration and Reliability Improvement (pp. 177–178).Google Scholar
  17. Fenton, N. E., & Neil, M. (1999). A critique of software defect prediction models. IEEE Transaction on Software Engineering, 25(5), 675–689.CrossRefGoogle Scholar
  18. Harter, D. E., Krishnan, M. S., & Slaughter, S. A. (2000). Effects of process maturity on quality, cycle time and effort in software product development. Management Science, 46, 451–466.CrossRefGoogle Scholar
  19. Pressman, R. S. (2005). Software engineering: A practitioner’s approach (6th ed.). New York: McGraw-Hill Publication.Google Scholar
  20. Yadav, O. P., Singh, N., Chinnam, R. B., & Goel, P. S. (2003). A fuzzy logic based approach to reliability improvement during product development. Reliability Engineering and System Safety, 80, 63–74.CrossRefGoogle Scholar
  21. Xie, M., Hong, G. Y., & Wohlin, C. (1999). Software reliability prediction incorporating information from a similar project. The Journal of Systems and Software, 49, 43–48.CrossRefGoogle Scholar
  22. Zadeh, L. A. (1989). Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering, 1, 89–100.CrossRefGoogle Scholar
  23. Bowles, J. B., & Pelaez, C. E. (1995). Application of fuzzy logic to reliability engineering. Proceedings of IEEE, 83(3), 435–449.CrossRefGoogle Scholar
  24. Mamdani, E. H. (1977). Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transaction on Computers, 26(12), 1182–1191.MATHCrossRefGoogle Scholar
  25. Kumar, K. S. (2009). Early software reliability and quality prediction (Ph.D. Thesis, IIT Kharagpur, Kharagpur, India).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Engineering and Manufacturing ServicesCognizant Technology SolutionHyderabadIndia
  2. 2.Reliability Engineering CentreIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations