The ABLoTS Approach for Bug Localization: is it replicable and generalizable?
The ABLoTS Approach for Bug Localization: is it replicable and generalizable?
复制标题
DOI：
10.1109/msr59073.2023.00083
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Feifei Niu;Christoph Mayr-Dorn;W. K. Assunção;LiGuo Huang;Jidong Ge;Bin Luo;Alexander Egyed
中科院分区：
文献类型：
--
作者：
Feifei Niu;Christoph Mayr-Dorn;W. K. Assunção;LiGuo Huang;Jidong Ge;Bin Luo;Alexander Egyed
错误定位是推荐源代码位置（通常是文件）的任务，这些位置可能包含错误的原因，因此需要更改以修复错误。沿着这些思路，采用了基于信息检索的错误定位（IRBL）方法，该方法从源代码空间中识别最容易出现错误的文件。在目前的实践中，一系列最先进的IRBL技术利用不同组件的组合，例如，类似的报表、版本历史、代码结构，以实现更好的性能。ABLoTS是最近提出的一种方法，其核心组件TraceScore利用不同问题报告之间的需求和可追溯性信息，即，功能请求和错误报告，以识别错误的源代码片段，并获得有希望的结果。为了评估这些结果的准确性，并获得对ABLoTS实用性的更多见解，支持未来更有效，更快速的复制和比较，我们使用原始数据集和扩展数据集对这种方法进行了复制研究。扩展数据集包括另外16个项目，包括25，893个错误报告和相应的源代码提交。虽然我们发现作为ABLoTS核心的TraceScore组件产生了与扩展数据集相当的结果，但我们也发现ABLoTS方法不再取得有希望的结果，这是由于错误选择截止日期的副作用被忽视，导致训练数据泄漏到测试数据中，对性能产生重大影响。
Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.