Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.

中文翻译：

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DEJIT：一种用于努力感知即时软件缺陷预测的差分进化算法

软件缺陷预测是节省测试资源、提高软件质量的有效途径，在软件工程领域得到广泛研究。工作量感知的即时软件缺陷预测 (JIT-SDP) 旨在识别有限软件测试资源中的缺陷软件更改。尽管已经提出了许多解决 JIT-SDP 的方法，但现有模型的努力感知预测性能仍有待进一步提高。为此，我们提出了一种基于差分进化 (DE) 的监督方法 DEJIT 来构建 JIT-SDP 模型。具体来说，首先我们提出一个称为密度百分比平均值(DPA)，用作训练集的优化目标。然后，我们使用逻辑回归（LR）来构建预测模型。为了使LR在训练集上获得最大的DPA，我们使用DE算法来确定LR的系数。该实验使用来自六个开源项目的缺陷数据集。我们在交叉验证、跨项目验证和时间交叉验证场景中将所提出的方法与最先进的四个监督模型和四个无监督模型进行了比较。实证结果表明，DEJIT 方法可以显着提高三种评估场景中的努力感知预测性能。因此，DEJIT 方法对于努力感知的 JIT-SDP 很有前景。