Throughout its development period, a software project experiences different phases, comprises modules with different complexities and is touched by many different developers. Hence, it is natural that problems such as Just-in-Time Software Defect Prediction (JIT-SDP) are affected by changes in the defect generating process (concept drifts), potentially hindering predictive performance. JIT-SDP also suffers from delays in receiving the labels of training examples (verification latency), potentially exacerbating the challenges posed by concept drift and further hindering predictive performance. However, little is known about what types of concept drift affect JIT-SDP and how they affect JIT-SDP classifiers in view of verification latency. This work performs the first detailed analysis of that. Among others, it reveals that different types of concept drift together with verification latency significantly impair the stability of the predictive performance of existing JIT-SDP approaches, drastically affecting their reliability over time. Based on the findings, a new JIT-SDP approach is proposed, aimed at providing higher and more stable predictive performance (i.e., reliable) over time. Experiments based on ten GitHub open source projects show that our approach was capable of produce significantly more stable predictive performances in all investigated datasets while maintaining or improving the predictive performance obtained by state-of-art methods.

中文翻译：

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实现可靠的在线即时软件缺陷预测


在整个开发过程中，软件项目会经历不同的阶段，包含不同复杂程度的模块，并受到许多不同开发人员的接触。因此，诸如即时软件缺陷预测 (JIT-SDP) 之类的问题自然会受到缺陷生成过程变化（概念漂移）的影响，从而可能阻碍预测性能。 JIT-SDP 还存在接收训练示例标签的延迟（验证延迟），这可能会加剧概念漂移带来的挑战，并进一步阻碍预测性能。然而，关于哪些类型的概念漂移会影响 JIT-SDP 以及它们如何影响 JIT-SDP 分类器（考虑到验证延迟），人们知之甚少。这项工作对此进行了首次详细分析。其中，它揭示了不同类型的概念漂移和验证延迟会严重损害现有 JIT-SDP 方法预测性能的稳定性，随着时间的推移，会极大地影响其可靠性。基于这些发现，提出了一种新的 JIT-SDP 方法，旨在随着时间的推移提供更高、更稳定的预测性能（即可靠）。基于 10 个 GitHub 开源项目的实验表明，我们的方法能够在所有研究的数据集中产生显着更稳定的预测性能，同时保持或提高通过最先进的方法获得的预测性能。