Yuki Ueda

Yuki Ueda, Takashi Ishio, Akinori Ihara et al.

Static analysis tools, or linters, detect violation of source code conventions to maintain project readability. Those tools automatically fix specific violations while developers edit the source code. However, existing tools are designed for the general conventions of programming languages. These tools do not check the project/API-specific conventions. We propose a novel static analysis tool DevReplay that generates code change patterns by mining the code change history, and we recommend changes using the matched patterns. Using DevReplay, developers can automatically detect and fix project/API-specific problems in the code editor and code review. Also, we evaluate the accuracy of DevReplay using automatic program repair tool benchmarks and real software. We found that DevReplay resolves more bugs than state-of-the-art APR tools. Finally, we submitted patches to the most popular open-source projects that are implemented by different languages, and project reviewers accepted 80% (8 of 10) patches. DevReplay is available on https://devreplay.github.io.

Dong Wang, Yuki Ueda, Raula Gaikovina Kula et al.

Code Review (CR) is the cornerstone for software quality assurance and a crucial practice for software development. As CR research matures, it can be difficult to keep track of the best practices and state-of-the-art in methodology, dataset, and metric. This paper investigates the potential of benchmarking by collecting methodology, dataset, and metric of CR studies. A systematic mapping study was conducted. A total of 112 studies from 19,847 papers published in high-impact venues between the years 2011 and 2019 were selected and analyzed. First, we find that empirical evaluation is the most common methodology (65% of papers), with solution and experience being the least common methodology. Second, we highlight 50% of papers that use the quantitative method or mixed-method have the potential for replicability. Third, we identify 457 metrics that are grouped into sixteen core metric sets, applied to nine Software Engineering topics, showing different research topics tend to use specific metric sets. We conclude that at this stage, we cannot benchmark CR studies. Nevertheless, a common benchmark will facilitate new researchers, including experts from other fields, to innovate new techniques and build on top of already established methodologies. A full replication is available at https://naist-se.github.io/code-review/.