RJXB软件学报Journal of Software1000-9825软件学报编辑部中国北京rjxb-32-2-24710.13328/j.cnki.jos.006130TP311系统软件与软件工程System Software and Software Engineering基于信息检索的软件缺陷定位方法综述Survey on Information Retrieval-based Software Bug Localization Methods李政亮LIZheng-Liang
Information retrieval-based software bug localization is an active research topic in the domain of software fault localization. It first analyzes the contents of the bug reports and program modules. Then it calculates the similarity between the bug reports and program modules. Finally, it recommends the most similar program modules to developers when given a bug report. This paper presents a systematic survey of existing research achievements of the domestic and international researchers in recent years. First, a research framework is proposed and three key factors (i.e., data sources, retrieval model, and application scenario), which may influence the performance of bug localization methods are identified. Next, existing research achievements in these three key factors are discussed in sequence. Then, the performance evaluation measures and datasets commonly used in information retrieval-based bug localization are summarized. Finally, conclusions of this study are drawn and a perspective of the future work in this research area is discussed.
软件维护软件缺陷定位信息检索缺陷报告程序模块software maintenancesoftware bug localizationinformation retrievalbug reportprogram module国家自然科学基金61972192国家自然科学基金61202006国家自然科学基金61906085国家自然科学基金41972111第2次青藏高原综合科学考察研究项目2019QZKK0204南京大学计算机软件新技术国家重点实验室开放课题KFKT2019B14南京大学计算机软件新技术国家重点实验室开放课题KFKT2018B17国家自然科学基金(61972192,61202006,61906085,41972111);第2次青藏高原综合科学考察研究项目(2019QZKK0204);南京大学计算机软件新技术国家重点实验室开放课题(KFKT2019B14,KFKT2018B17)National Natural Science Foundation of China61972192National Natural Science Foundation of China61202006National Natural Science Foundation of China61906085National Natural Science Foundation of China41972111Second Tibetan Plateau Scientific Expedition and Research Program2019QZKK0204Open Project of State Key Laboratory for Novel Software Technology at Nanjing UniversityKFKT2019B14Open Project of State Key Laboratory for Novel Software Technology at Nanjing UniversityKFKT2018B17National Natural Science Foundation of China (61972192, 61202006, 61906085, 41972111); Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0204); Open Project of State Key Laboratory for Novel Software Technology at Nanjing University (KFKT2019B14, KFKT2018B17)
Sisman等人[27]首次使用版本信息来估计程序模块的先验缺陷概率, 用于缺陷定位. 他们基于缺陷预测的已有研究工作, 认为文件修改历史和文件缺陷历史是比较好的文件潜在缺陷预测特征. 因此, 他们定义了修改历史先验(modification history based prior, 简称MHbP)和缺陷历史先验(defect history based prior, 简称DHbP)两个指标, 其计算公式如下.
(2) Top K Rank: 该指标在有些文献中又被称为Accuracy@k[75]、Hit@k[62]、Prediction Accuracy[25]、Recall at Top N[33]等, 其返回正确定位的缺陷报告占所有缺陷报告的比例. 正确定位的缺陷报告指在推荐的前k(k=1, 5, 10)个程序模块中至少包含一个缺陷程序模块, 在有些文献中, 这个指标返回的是正确定位的缺陷报告的个数, 这里, 我们把该指标统一为比例.
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