Abstract
Software Defect Prediction is based on datasets that are imbalanced and therefore limit the use of machine learning based classification. Ensembles of genetic classifiers indicate good performance and provide a promising solution to this problem. To further examine this solution, we performed additional experiments in that direction. In this paper we report preliminary results obtained by using a Matlab variant of NSGA-II in combination with four simple voting strategies on three subsequent releases of the Eclipse Plug-in Development Environment (PDE) project. Preliminary results indicate that the voting procedure might influence software defect prediction performances.
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References
Abraham, A., Goldberg, R.: Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. Springer, Heidelberg (2006). Science & Business Media
Afzal, W., Torkar, R.: A comparative evaluation of using genetic programming for predicting fault count data. In: ICSEA 2008, pp. 407–414 (2008)
Bhowan, U., Johnston, M., Zhang, M., Yao, X.: Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE TEC 17(3), 368–386 (2013)
Coello Coello, C., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer, Berlin (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TEC 6(2), 182–197 (2002)
Ferrucci, F., Gravino, C., Oliveto, R., Sarro, F.: Genetic programming for effort estimation: an analysis of the impact of different fitness functions. In: SSBSE 2010, pp. 89–98 (2010)
Galinac Grbac, T., Mauša, G., Dalbelo Bašić, B.: Stability of software defect prediction in relation to levels of data imbalance. In: SQAMIA (2013)
Harman, M., McMinn, P.: A theoretical and empirical study of search based testing: local. global and hybrid search. IEEE TSE 36(2), 226–247 (2010)
Mauša, G., Galinac Grbac, T., Dalbelo Bašić, B.: Software defect prediction with bug-code analyzer - a data collection tool demo. In: SoftCOM 2014 (2014)
Sarro, F., Di Martino, S., Ferrucci, F., Gravino, C.: A further analysis on the use of genetic algorithm to configure support vector machines for inter-release fault prediction. In: SAC 2012, pp. 1215–1220
Sarro, F., Ferrucci, F., Gravino, C.: Single and multi objective genetic programming for software development effort estimation. In: ACM SAC 2012, pp. 1221–1559 (2012)
Shin, Y., Harman, M.: Pareto efficient multiobjective test case selection. In: ISSTA 2007, pp. 140–150 (2007)
Wang, S., Yao, X.: Using class imbalance learning for software defect prediction. IEEE Trans. Reliab. 62(2), 434–443 (2013)
Acknowledgments
The work presented in this paper is supported by the University of Rijeka Research Grant 13.09.2.2.16.
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Rubinić, E., Mauša, G., Grbac, T.G. (2015). Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_33
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DOI: https://doi.org/10.1007/978-3-319-22183-0_33
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