Fault Prediction based on Software Metrics and SonarQube Rules. Machine or Deep Learning?
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Background. Developers spend more time fixing bugs and refactoring the code to increase the maintainability than developing new features. Researchers investigated the code quality impact on fault-proneness focusing on code smells and code metrics. Objective. We aim at advancing fault-inducing commit prediction based on SonarQube considering the contribution provided by each rule and metric. Method. We designed and conducted a case study among 33 Java projects analyzed with SonarQube and SZZ to identify fault-inducing and fault-fixing commits. Moreover, we investigated fault-proneness of each SonarQube rule and metric using Machine and Deep Learning models. Results. We analyzed 77,932 commits that contain 40,890 faults and infected by more than 174 SonarQube rules violated 1,9M times, on which there was calculated 24 software metrics available by the tool. Compared to machine learning models, deep learning provide a more accurate fault detection accuracy and allowed us to accurately identify the fault-prediction power of each SonarQube rule. As a result, fourteen of the 174 violated rules has an importance higher than 1% and account for 30% of the total fault-proneness importance, while the fault proneness of the remaining 165 rules is negligible. Conclusion. Future works might consider the adoption of timeseries analysis and anomaly detection techniques to better and more accurately detect the rules that impact fault-proneness.
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