论文标题
功能集以恰当的缺陷预测：经验评估
Feature Sets in Just-in-Time Defect Prediction: An Empirical Evaluation
论文作者
论文摘要
即时缺陷预测为每个新更改为软件存储库分配了缺陷风险，以便优先考虑审核和测试工作。在过去的几十年中，文献中提出了不同的方法来制定更准确的预测模型。但是，由于性能变化，缺陷预测仍未在行业中广泛使用。在这项研究中，我们评估了六个开源项目的现有功能，并提出了两个新功能集，尚未在文献中进行讨论。通过结合所有功能集，我们平均将MCC提高了21％，与最新方法相比，可以提高性能最佳。我们还评估了努力意识，并发现平均可以识别出14％的缺陷，从而检查了20％的变化线。
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%, leading to the best performing models when compared to state-of-the-art approaches. We also evaluate effort-awareness and find that on average 14% more defects can be identified, inspecting 20% of changed lines.
