A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents

Authors

  • Bondyopadhyay A Department of Computer Science, Mankar College, Burdwan, India

Keywords:

Cost,, Classification, Intelligent agent, Data mining, Database, Defect, Testing

Abstract

Defect prediction for a software system is a technique that is used extensively nowadays to predict defects from historical database. But only a good data mining model is not enough to extract defect from software bug record. Intelligent agents are helpful in this case by making the decision process easier at some point. This paper describes frame work to generate software defect from the historical database and also propose one algorithm that is used find policy to forecast software defects efficiently than the current methods

References

[1] Ms. P.J Kaur, Ms. Pallavi, “Data Mining Techniques for Software Defect Prediction” , International Journal of Software and Ib Sciences (IJSWS)

[2] W Sunindyo, T Moser, D Winkler , “Improving Open Source Software Process Quality based on Defect Data Mining” , Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems,Vienna University of Technology.

[3] K.B.S Sastry, Dr.B.V.Subba Rao, Dr K.V.Sambasiva Rao, “Software Defect Prediction from Historical Data” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2013

[4] N Azeem, S Usmani, “Analysis of Data Mining Based Software Defect Prediction Techniques”,Global Journal of Computer Science and Technology, Volume 11 Issue 16 Version 1.0 September 2011

[5] T Xie, S Thummalapenta, D Lo, and C Liu, “Data mining for software engineering.” Computer, 42(8):55-62, 2009.

[6] M Baojun, K Dejaeger, J Vanthienen, and B Baesens, “Software defect prediction based on association rule classification” SSRN 1785381, 2011.

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Published

2025-11-24

How to Cite

[1]
A. Bondyopadhyay, “A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 245–248, Nov. 2025.

Issue

Vol. 7 No. 1 (2019): IJCSE Special Issue Jan Edition

Section

Review Article

License

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