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An empirical study of ensemble techniques for software fault prediction

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Abstract

Previously, many researchers have performed analysis of various techniques for the software fault prediction (SFP). Oddly, the majority of such studies have shown the limited prediction capability and their performance for given software fault datasets was not persistent. In contrast to this, recently, ensemble techniques based SFP models have shown promising and improved results across different software fault datasets. However, many new as well as improved ensemble techniques have been introduced, which are not explored for SFP. Motivated by this, the paper performs an investigation on ensemble techniques for SFP. We empirically assess the performance of seven ensemble techniques namely, Dagging, Decorate, Grading, MultiBoostAB, RealAdaBoost, Rotation Forest, and Ensemble Selection. We believe that most of these ensemble techniques are not used before for SFP. We conduct a series of experiments on the benchmark fault datasets and use three distinct classification algorithms, namely, naive Bayes, logistic regression, and J48 (decision tree) as base learners to the ensemble techniques. Experimental analysis revealed that rotation forest with J48 as the base learner achieved the highest precision, recall, and G-mean 1 values of 0.995, 0.994, and 0.994, respectively and Decorate achieved the highest AUC value of 0.986. Further, results of statistical tests showed used ensemble techniques demonstrated a statistically significant difference in their performance among the used ones for SFP. Additionally, the cost-benefit analysis showed that SFP models based on used ensemble techniques might be helpful in saving software testing cost and effort for twenty out of twenty-eight used fault datasets.

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Notes

  1. https://sites.google.com/site/santoshiiitmdj/software-fault-datasets?authuser=0

  2. TP = True positive, FP = False positive, FN = False negative, TN = True negative, N = Negative

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Acknowledgments

We are thankful to the editor and the anonymous reviewers for their valuable comments that helped in improvement of the paper.

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  1. Department of Information Technology, ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Santosh S. Rathore

  2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Sandeep Kumar

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Appendix

Appendix

In this study, we have used Weka implementation of used ensemble techniques and base learners. Following parameter values have been set for these three base learning algorithms amd seven ensemble techniques.

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Rathore, S.S., Kumar, S. An empirical study of ensemble techniques for software fault prediction. Appl Intell 51, 3615–3644 (2021). https://doi.org/10.1007/s10489-020-01935-6

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