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Title: Software Defect Prediction Using Regression via Classification
Author(s): S. Bibi, G. Tsoumakas, I. Stamelos, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (7 pages).
Appeared in: Proc. 4th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA '06, (accepted for presentation), pp. 330- 336, 2006.
Abstract: In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discretizes the number of defects into a number of fault classes, then learns a model that predicts the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies, with a certain confidence. To evaluate this approach we perform a comparative experimental study of the effectiveness of several machine learning algorithms in a software dataset. The data was collected by Pekka Forselious and involves applications maintained by a bank of Finland.
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        This paper has been cited by the following:

1 M. Demir, "Predicting Component Failures at Early Design Time", Master's Thesis, Department of Computer Science, Faculty of Natural Sciences and Technology I, Saarland University, 2006.
2 Matalonga, S., Gilabert, T.S.F. (2008) Linking Return on Training Investment with Defects Causal Analysis. Proceedings of the Twentieth International Conference on Software Engineering and Knowledge Engineering (SEKE'2008), pp. 42-47.
3 Kläs, M., Nakao, H., Elberzhager, F., Münch, J. "Predicting defect content and quality assurance effectiveness by combining expert judgment and defect data - A case study", Proc. International Symposium on Software Reliability Engineering, ISSRE, 2008, pages 17-26.
4 Cagatay Catal, Banu Diri, A systematic review of software fault prediction studies, Expert Systems with Applications, Volume 36, Issue 4, May 2009, Pages 7346-7354.
5 Kläs, M., Nakao, H., Elberzhager, F., Münch, J. (2010) "Support planning and controlling of early quality assurance by combining expert judgment and defect data – a case study", Empirical Software Engineering 15, August 2010.
6 724. Gupta,K.;Kang,S.;Sandhu,P.S. (2011) Comparison of Resilient Backpropagation and Fuzzy Clustering Based Approach for Prediction of Level of Severity of Faults in Software Systems, Planetary Scientific Research Center Proceeding, July 2011 Bangkok ISBN:978-81-921733-1-3 .
7 Benson, M.J. (2011) Toward intelligent software defect detection: Learning software defects by example, Proceedings - 2011 34th IEEE Software Engineering Workshop, SEW 2011, art. no. 6146920, pp. 138-142.
8 Kaur,K. (2012), International Journal of Information Technology and Knowledge Management, Analysis of Resilient Back-Propagation for Improving Software Process Control, July-December 2012, Volume 5, No. 2, pp. 377-379
9 Kaur,K. (2012) Empirical Analysis of Fault Predication Techniques for Improving Software Process Control, International Journal of Information Technology and Knowledge Management, July-December 2012, Volume 5, No. 2, pp. 371-375
10 Banthia, D., Gupta, A. (2012) Investigating fault prediction capabilities of five prediction models for software quality, Proceedings of the ACM Symposium on Applied Computing, pp. 1259-1261.