https://doi.org/10.1145/3511430.3511463

Oishie, N. Z., and Roy, B. (2022) Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models, in Proceedings of the ACM 15th Innovation in Software Engineering Conference (ISEC 2022), ACM Student Research Competition (SRC), Article 36, pp. 1-3. https://doi.org/10.1145/3511430.3511463

When developing new software, testing can take up half of the resources. Although a considerable amount of work has been done to automate software testing, fixing bugs after adding them to the source repository is still a costly task from both management and financial perspectives. In recent times, the research community has proposed various methodologies to detect bugs just-in-time at the commit level. Unfortunately, this work, including state-of-the-art techniques, do not provide real-time solutions for the problem. Such a limitation restricts developers from utilizing them in their day-to-day programming tasks. Our study focuses on providing solutions that deliver real-time support to the developers by warning them about potential bug-inducing commits. Such support can help developers by preventing them from adding a bug-inducing commit to the source repository. Keeping this goal in mind, we conducted a developer survey to understand the expectations of developers for bug-inducing commit detection tools. Motivated by their responses, we built a GUI-based plug-in that warns the developers when they attempt to perform a potential buggy commit. We accomplished this by training machine learning models on relevant features. We also built a command-line tool for the developers who prefer to use a command-line interface. Our proposed solution has been designed to work with various machine learning models (e.g. random forest, decision tree, and logistic regression) and IDEs (e.g. Visual Studio, PyCharm, and WebStorm). It enables developers to work with a familiar interface without leaving the IDE. As a proof of concept, we implemented a VSCode plug-in and an accompanying command-line tool. Developers can customize these tools by choosing among various machine learning models and features. Such customizability empowers the developers to understand the toolchain better and lets them fit it into their specific use cases. Our user study shows that the toolchain offers satisfactory performance in detecting bug-inducing commits and provides a sound user experience. The decision tree model achieved the best performance with a 79% accuracy and an f1-score of 0.70 among the tested models. In addition, we performed a user study with developers working in the software industries to validate the usability of our toolchain. We found that the users can detect whether a commit is bug-inducing or not within a short period of time. Furthermore, they prefer our tool over the state-of-the-art to detect potential bugs before the commit operation. Alongside contributing a new multi-UI toolchain, our work enriches the research community’s knowledge regarding developer usability of real-time bug detection tools.

Computer Science Core Team, Refereed Publications

https://doi.org/10.1145/3511430.3511463
https://hdl.handle.net/10388/13802