ENSEMBLE OF SOFTWARE DEFECT PREDICTORS: AN AHP-BASED EVALUATION METHOD

Yi Peng, Gang Kou (), Guoxun Wang, Wenshuai Wu and Yong Shi
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Yi Peng: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P. R. China, 610054, P. R. China
Gang Kou: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P. R. China, 610054, P. R. China
Guoxun Wang: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P. R. China, 610054, P. R. China
Wenshuai Wu: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P. R. China, 610054, P. R. China
Yong Shi: College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA;

International Journal of Information Technology & Decision Making (IJITDM), 2011, vol. 10, issue 01, 187-206

Abstract: Classification algorithms that help to identify software defects or faults play a crucial role in software risk management. Experimental results have shown that ensemble of classifiers are often more accurate and robust to the effects of noisy data, and achieve lower average error rate than any of the constituent classifiers. However, inconsistencies exist in different studies and the performances of learning algorithms may vary using different performance measures and under different circumstances. Therefore, more research is needed to evaluate the performance of ensemble algorithms in software defect prediction. The goal of this paper is to assess the quality of ensemble methods in software defect prediction with the analytic hierarchy process (AHP), which is a multicriteria decision-making approach that prioritizes decision alternatives based on pairwise comparisons. Through the application of the AHP, this study compares experimentally the performance of several popular ensemble methods using 13 different performance metrics over 10 public-domain software defect datasets from the NASA Metrics Data Program (MDP) repository. The results indicate that ensemble methods can improve the classification results of software defect prediction in general and AdaBoost gives the best results. In addition, tree and rule based classifiers perform better in software defect prediction than other types of classifiers included in the experiment. In terms of single classifier, K-nearest-neighbor, C4.5, and Naïve Bayes tree ranked higher than other classifiers.

Keywords: Ensemble; classification; software defect prediction; the analytic hierarchy process (AHP) (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (16)

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DOI: 10.1142/S0219622011004282

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