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RETRACTED ARTICLE: Exponential Relationship Based Approach for Predictions of Defect Density Using Optimal Module Sizes

  • Dinesh Kumar Verma
  • Shishir KumarEmail author
Research Article

Abstract

Although, a number of researches have been taken to predict software defect density, analysis of dependency of this attribute on particular factor is need of researchers. In the recent time, software industries applying more efforts in the testing, for identifying and fixing the bugs early in the software development process. Software defect density is considered as the parameter validating the readiness of software product before release. Using this parameter, developers can identify the possibilities of improvements in the product. Most of the projects reflect a dependency of defect density on the size of the modules. The nature of dependency, need to be analyzed for better prediction of defect density. In this paper analyzed with optimal values of parameters by considering the logarithmic nature of dependency. These optimal values of parameter gave a better prediction of defect density by varying in module sizes. Analysis of the proposed model was performed on the available data sets and compared with the model given by Verma and Kumar. The curve fitting method was used to drive the optimal values of parameters. At the end it inferred that the prediction of defect density could be optimized by effective distribution of size of modules. The exponential relationship between module size and defect density give more optimized defect density.

Keywords

Module Size Defect density Min–max criteria Method of least square Curve fitting 

References

  1. 1.
    Koru A, Liu H (2005) Building effective defect-prediction models in practice. IEEE Softw 22(6):23–29CrossRefGoogle Scholar
  2. 2.
    Fenton NE, Ohlsson N (2000) Quantitative analysis of faults and failures in a complex software system. IEEE Trans Softw Eng 26(8):797–814CrossRefGoogle Scholar
  3. 3.
    Koru A, Tian J (2003) An empirical comparison and characterization of high defect and high complexity modules. J Syst Softw 67(3):53–163Google Scholar
  4. 4.
    Staron M, Meding W (2008) Predicting weekly defect inflow in large software projects based on project planning and test status. Inf Softw Technol 50(7–8):782–796CrossRefGoogle Scholar
  5. 5.
    Takahashi M, Kamayachi Y (1985) An empirical study of a model for program error prediction. In: Proceedings of 8th international IEEE conference on software engineering, pp 330–333Google Scholar
  6. 6.
    Munson JC, Khoshgoftar TM (1996) Software metrics in reliability assessment. In: Lyu MR (ed) Handbook of software reliability engineering. IEEE-CS Press/McGraw-Hill, New YorkGoogle Scholar
  7. 7.
    Farr W (1996) Software reliability modeling survey. In: Lyu MR (ed) Handbook of Software Reliability Engineering. IEEE-CS Press/McGraw-Hill, New YorkGoogle Scholar
  8. 8.
    Malaiya YK, Denton JA (1997) What do software reliability parameters represent? In: Proceeding international symposium on software reliability engineering, pp 124–135Google Scholar
  9. 9.
    Li N, Malaiya YK (1995) ROBUST: a next generation software reliability engineering tool. In: Proceeding IEEE international symposium on software reliability engineering, pp 375–380Google Scholar
  10. 10.
    Ferdinand AE (1974) A theory of system complexity. Int J General Syst 1(1):19–33CrossRefGoogle Scholar
  11. 11.
    Malaiya YK, Denton JA (2000) Module size distribution and defect density. In: Proceedings of 11th international symposium on software reliability engineering. ISSRE 2000, pp 62–71Google Scholar
  12. 12.
    Verma D, Kumar S (2014) An improved approach for reduction of defect density using optimal module sizes. Hindwai Publishing Corporation, Adv Softw Eng, vol 2014, Article ID 803530Google Scholar
  13. 13.
    Fenton N, Neil M (1999) A critique of software defect prediction research. IEEE Trans Softw Eng 25(5):675–689CrossRefGoogle Scholar
  14. 14.
    Basili VR, Perricone BR (1984) Software errors and complexity. Commun ACM 27:42–52CrossRefGoogle Scholar
  15. 15.
    Withrow C (1990) Error density and size in Ada software. IEEE Softw 7(1):26–30CrossRefGoogle Scholar
  16. 16.
    Banker RD, Kemerer CF (1989) Scale economies in new software development. IEEE Trans Softw Eng 15(10):1199–1205CrossRefGoogle Scholar
  17. 17.
    Rosenberg J (1997) Some misconceptions about lines of code. In: Proceedings of international software metrics symposium, pp 137–142Google Scholar
  18. 18.
    Fenton N et al (2001) A probabilistic model for software defect prediction. IEEE Trans Softw Eng 44:1–35Google Scholar
  19. 19.
    Hatton L (1997) Re-examining the fault density component size connection. IEEE Softw 14(2):89–97CrossRefGoogle Scholar
  20. 20.
  21. 21.
    SPSS IBM SPSS statistics version 20 64-bit. http://www.spss.com/statistics
  22. 22.
    http://promisedata.org. Accessed on 29 July 2013

Copyright information

© The National Academy of Sciences, India 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceJaypee University of Engineering and TechnologyGunaIndia

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