Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and Popt of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

中文翻译：

---

一种新的工作量测量方法，用于工作量感知的即时软件缺陷预测

即时软件缺陷预测（JIT-SDP）是一种细粒度的软件缺陷预测技术，旨在识别软件系统中的缺陷代码变更。Effort-aware软件缺陷预测是一种考虑代码检查成本的软件缺陷预测技术，可以在有限的测试资源中发现更多有缺陷的代码变化。传统的工作量感知缺陷预测模型主要根据代码行数（LOC）来衡量工作量，很少考虑其他因素。本文提出了一种新的努力度量方法，称为多度量联合计算 (MMJC)。在衡量工作量时，MMJC 不仅考虑 LOC，还考虑修改后代码在不同文件中的分布（熵），更改文件的开发人员数量 (NDEV) 和开发人员体验 (EXP)。在仿真实验中，MMJC分别与线性回归、决策树、随机森林、LightGBM、支持向量机和神经网络相结合，构建软件缺陷预测模型。在基于 MMJC 的模型和基线模型之间进行了几个比较实验。结果表明，指标 ACC 和磷选择与基线模型相比，基于 MMJC 的模型在三个验证场景中分别平均提高了 35.3% 和 15.9%。