Automatically Generating Commit Messages from Diffs using Neural Machine Translation
This addresses the issue of neglected commit messages in software development, offering an automated tool to improve documentation, though it is incremental as it builds on existing NMT techniques with a focus on quality filtering.
The paper tackles the problem of automatically generating commit messages from code diffs by adapting Neural Machine Translation (NMT) to translate diffs into messages, achieving a pattern where generated messages are either very high or very low quality, leading to the creation of a quality-assurance filter to detect and warn about poor outputs.
Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically "translate" diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.