Browse > Article

An Estimation of Risky Module using SVM  

Kim, Young-Mi (한국원자력안전기술원 공학연구실)
Jeong, Choong-Heui (한국원자력안전기술원 공학연구실)
Kim, Hyeon-Soo (충남대학교 전기정보통신공학부 컴퓨터)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.6, 2009 , pp. 435-439 More about this Journal
Abstract
Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules of safety-critical software, we can focus testing activities and regulation activities more efficiently such as resource distribution. In this paper, we classified the estimated risk class which can be used for deep testing and V&V. We predicted the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 and 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews.
Keywords
Safety-Critical Software; Software Dependability; SVM; Software Testing; Software V&V;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Burges. C., A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Know-ledge Discovery, 1998   DOI   ScienceOn
2 Khoshgoftaar, TM., Munson, J.C., Prediction soft-ware development errors using complexity metrics. IEEE Journal on Selected Areas in Communi-cations, Volume 8, Issue 2, pp. 153-261, 1990   DOI   ScienceOn
3 Boehm, B.W., Papaccio, P.N., Understanding and controlling software costs. IEEE Transcations on Software Engineering, Volume 14, Issue 10, pp. 1462-1477, 1988   DOI   ScienceOn
4 Xing, F., Guo, P., Lyu, M.R., A novel method for early software quality prediction based on support vector machine, Proceedings of IEEE International Conference on Software Reliability Engineering, pp. 213-222, 2005   DOI
5 T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods-Support Vector Learning, B. Scholkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999
6 Porter, A., Selby, H., Empirically guided software development using metric-based classification trees. IEEE Software, Volume 7, Issue 2, pp. 46-54, 1990   DOI   ScienceOn
7 http://svmlight.joachims.org/svm_multiclass.html
8 Briand, L.T., Basili, V.R., Hetmanski, C., Deve-loping interpretable models for optimized set reduc-tion for identifying high-risk software components. IEEE Transactions on Software Engineering, Volume 19, Issue 11, pp. 1028-1034, 1993   DOI   ScienceOn
9 Khoshgoftaar, T.M., Lanning, D,L., Pandya, A.S., A comparative study of pattern recognition tech-niques for quality evaluation of telecommunica-tions software, IEEE Journal on Selected Areas in Communications, Volume 12, Issue 2, pp. 208-217, 1994   DOI   ScienceOn
10 Karim O. Elish, Mahmoud O. Elish, Predicting defect-prone software modules using support vec-tor machines, The Journal of Systems and Software, Volume 81, Issue 5, pp. 649-660, 2008   DOI   ScienceOn
11 Steve R, Gunn, Support Vector Machines for Cla-ssification and Regression, Technical Report, Uni-versity of Southampton, 10 May 1998
12 I. Gondra, Applying machine learning to software fault-proness prediction, The Journal of Systems and Software, Volume 81, Issue 2, pp. 186-195, 2008   DOI   ScienceOn