Skip to main content

A Fuzzy Logic Approach for Multistage Defects Density Analysis of Software

  • Conference paper
  • First Online:
Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 336))

Abstract

The prediction of software defects in a software project has recently attracted the attention of many researchers. Prediction of defect density indicator (DDI) in each phase of software development life cycle (SDLC) is desirable for effective decision support and trade-off analysis during software development, and also, it improves the reliability of software project and helps software manager to achieve reliable software product within time and costs. The reliability-relevant software metrics impose major impact on the quality of software project at each software development stage. However, software metrics are associated with uncertainty and can be assessed in linguistic terms. Therefore, in this paper, a multistage model for software DDI is proposed using the topmost reliability-relevant metrics and fuzzy inference system (FIS).The predictive accuracy of proposed model is validated using real software projects data. Validation results are satisfactory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lyu, M.R.: Handbook of Software Reliability Engineering. McGraw-Hill, New York (1995)

    Google Scholar 

  2. ANSI/IEEE Std 729-1983 1, IEEE Standard Glossary of Software Engineering Terminology (1983)

    Google Scholar 

  3. IEEE Std 982.1-1988, IEEE Standard Dictionary of Measures to Produce Reliable Software (1988)

    Google Scholar 

  4. Musa, J.D., Iannino, A., Okumoto, K.: Software Reliability: Measurement, Prediction, Application. McGraw-Hill Publishers, New York (1987)

    Google Scholar 

  5. Kaner, C.: Software engineering metrics: what do they measure and how do we know? In: 10th International Software Metrics Symposium (2004)

    Google Scholar 

  6. Cai, K.Y., Wen, C.Y., Zhang, M.L.: A critical review on software reliability modeling. Reliab. Eng. Syst. Saf. 32, 357–371 (1991)

    Article  Google Scholar 

  7. Pham, H.: System Software Reliability. Reliability Engineering Series. Springer, London (2006)

    Google Scholar 

  8. Methodology for software reliability prediction and assessment, TechRep RL–TR-92-95, vols. 1–2. Rome laboratory (1992)

    Google Scholar 

  9. Methodology for software Prediction, RADAC-TR-87-171, 1987. USAF systems command (1987)

    Google Scholar 

  10. Agresti, W.W., Evanco, W.M.: Projecting software defects form analyzing Ada design. IEEE Trans. Softw. Eng. 18, 988–997 (1992)

    Article  Google Scholar 

  11. Wholin, C., Runeson, P.: Defect content estimations from review data. In: Proceedings of 20th International Conference on software Engineering, pp. 400–409 (1998)

    Google Scholar 

  12. Smidts, C., Stutzke, M., Stoddard, R.W.: Software reliability modeling: an approach to early reliability prediction. IEEE Trans. Reliab. 47, 268–278 (1998)

    Article  Google Scholar 

  13. Gaffney, J.E., Davis, C.F.: An approach to estimating software errors and availability. In: Proceedings of 11th Minnow Brook Workshop on Software Reliability (1988)

    Google Scholar 

  14. Gaffney, J.E., Pietrolewiez, J.: An automated model for software early error prediction (SWEEP). In: Proceedings of 13th Minnow Brook Workshop on Software Reliability (1990)

    Google Scholar 

  15. Gaffney Jr, J.E.: Estimating the number of faults in code. IEEE Trans. Softw. Eng. 10, 141–152 (1984)

    MathSciNet  Google Scholar 

  16. Lipow, M.: Number of faults per line of code. IEEE Trans. Softw. Eng. 8, 437–439 (1982)

    Article  Google Scholar 

  17. Khoshgoftaar, T.M., Musson, J.C.: Predicting software development errors using software complexity metrics. IEEE J. Sel. Areas Comm. 8, 253–261 (1990)

    Article  Google Scholar 

  18. Fenton, N.E., Neil, M.: A critique of software defect prediction models. IEEE Trans. Softw. Eng. 25, 675–689 (1999)

    Article  Google Scholar 

  19. Fenton, N.E., Neil, M., et al.: Predicting software defects in varying development lifecycles using Bayesian nets. Inf. Softw. Technol. 49, 32–43 (2007)

    Article  Google Scholar 

  20. Fenton, N.E., Neil, M., et al.: On the effectiveness of early life cycle defect prediction with Bayesian nets. Empirical Softw. Eng. 13, 499–537 (2008)

    Article  Google Scholar 

  21. Zhang, X., Pham, H.: An analysis of factors affecting software reliability. J. Syst. Softw. 50, 43–56 (2000)

    Article  Google Scholar 

  22. Li, M., Smidts, C., et al.: Ranking software engineering measures related to reliability using expert opinion. In: Proceedings of 11th International Symposium on Software Reliability Engineering (ISSRE), pp. 246–258, San Jose, California, 08 Oct 2000

    Google Scholar 

  23. Li, M., Smidts, C.: A ranking of software engineering measures based on expert opinion. IEEE Trans. Softw. Eng. 29, 811–824 (2003)

    Article  Google Scholar 

  24. Catal, C.: Software fault prediction: a literature review and current trends. Expert Syst. Appl. 38, 4626–4636 (2011)

    Article  Google Scholar 

  25. Radjenovic, D., et al.: Software fault prediction metrics: a systematic literature review. Inf. Softw. Technol. 55, 1397–1418 (2013)

    Article  Google Scholar 

  26. Pandey, A.K., Goyal, N.K.: A fuzzy model for early software fault prediction using process maturity and software metrics. Int. J. Electron. Eng. 1, 239–245 (2009)

    Google Scholar 

  27. Yadav, D.K., Charurvedi, S.K., Mishra, R.B.: Early software defects prediction using fuzzy logic. Int. J. Performability Eng. 8, 399–408 (2012)

    Google Scholar 

  28. Yadav, H.B., Yadav, D.K.: A multistage model for defect prediction of software development life cycle using fuzzy logic. In: Proceedings of the Third International Conference on Software Computing for Problem Solving, (SOCPROS-2013), 26–28 Dec 2013. Advances in Intelligent System and Computing, vol. 259, pp. 661–671. IIT Roorkee, India, Springer India Publication (2014)

    Google Scholar 

  29. Yadav, H.B., Yadav, D.K.: Defects prediction of early phases of software development life cycle using fuzzy logic. In: Proceedings of the 4th International Conference (CONFLUENCE 2013), pp. 2–6, 26–27 Sept 2013. The Next Generation Information Technology Summit, Amity University, Uttar Pradesh. IET Publications India 2013

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harikesh Bahadur Yadav .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Bahadur Yadav, H., Kumar Yadav, D. (2015). A Fuzzy Logic Approach for Multistage Defects Density Analysis of Software. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2220-0_10

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics