Feature Sets in Just-in-Time Defect Prediction: An Empirical Evaluation
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Just-in-time defect prediction assigns a defect risk to each new change to a software repository in order to prioritize review and testing efforts. Over the last decades different approaches were proposed in literature to craft more accurate prediction models. However, defect prediction is still not widely used in industry, due to predictions with varying performance. In this study, we evaluate existing features on six open-source projects and propose two new features sets, not yet discussed in literature. By combining all feature sets, we improve MCC by on average 21 compared to state-of-the-art approaches. We also evaluate effort-awareness and find that on average 14 changed lines.
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