Software Engineering Research Laboratory, Department of Mathematics and Computer Science, University of Quebec, Trois-Rivières, Qc, Canada.

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Abstract—In this paper, we propose new software agnostic metrics extracted from Git history. We compared the proposed metrics to many traditional code-based metrics in terms of fault severity prediction. We used three Machine Learning Algorithms (Random Forest, SVM and Multilayer Perceptron) to build the prediction models. We used data (source code, source code metrics, fault severity information) collected from three different data sources. Results show that the proposed software agnostic metrics perform better in terms of fault severity prediction compared to traditional code-based metrics. They were able to achieve 84% of accuracy in fault severity prediction. We also introduced some terms extracted from commits and showed their effectiveness for fault severity classification.

Index Terms—Bug tracking system, commit messages, fault severity classification, git metrics, machine learning.

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Cite: Herimanitra Ranaivoson, Mourad Badri, "Software Fault Severity Prediction Using Git History Metrics and Commits," Journal of Software vol. 17, no. 2, pp. 36-47, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)