RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information

By Tien-Duy B. Le, Mario Linares Vásquez, David Lo, Denys Poshyvanyk. 2015

47 citations recorded by the source corpus.

Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of various parts of a software system, predicting defects, and many other tasks. A number of existing approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages with textual contents in the bug reports. Yet, the effectiveness of these techniques is oftentimes sub optimal when commit messages are empty or only contain minimum information, this particular problem makes the process of recovering trace ability links between commits and bug reports particularly challenging. In this work, we aim at improving the effectiveness of existing bug linking techniques by utilizing rich contextual information. We rely on a recently proposed tool, namely Change Scribe, which generates commit messages containing rich contextual information by using a number of code summarization techniques. Our approach then extracts features from these automatically generated commit messages and bug reports and inputs them into a classification technique that creates a discriminative model used to predict if a link exists between a commit message and a bug report. We compared our approach, coined as RCLinker (Rich Context Linker), to MLink, which is an existing state-of-the-art bug linking approach. Our experiment results on bug reports from 6 software projects show that RCLinker can outperform MLink in terms of F-measure by 138.66%.

Subfields: Computer Networks and Communications, Information Systems, Software, Program Analysis & Verification

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, neutral): “ exploit these links to better understand and maintain their code. Usually, these links must be recovered. Information retrieval has dominated link recovery. The work of Guo et al. [79] and Le et al. [113] suggests that learning better (semantic) representations of artifacts can successfully, automa”
• Automatically generating commit messages from diffs using neural machine translation (2017, unknown): “For example, Le et al. have built RCLinker, which links a bug report to the corresponding commit message [31].”