Automatically Generating Commit Messages from Diffs using Neural Machine Translation (1708.09492v1)

Abstract: 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.

Automatically Generating Commit Messages from Diffs using Neural Machine Translation

This paper addresses the problem of generating succinct and high-quality commit messages from code diffs, an important task in software development. Well-crafted commit messages contribute significantly to the understanding of software evolution, providing insights into features added, bugs fixed, and overall code rationale. Despite their importance, programmers often fall short in crafting such messages effectively, opting for verbosity or omitting rationale.

The authors propose a novel approach utilizing Neural Machine Translation (NMT) to automate the generation of commit messages from diffs. This approach attempts to mimic the brevity and rational summary achieved by human authors, distinguishing it from existing methodologies that predominantly focus on detailing the ‘what’ rather than the ‘why’ of code changes.

Methodology

In developing their solution, the authors leverage a significant corpus from the top 1,000 projects on GitHub, encompassing both diffs and their corresponding human-written commit messages. Recognizing that not all commit messages provide equal value, a filtering process is applied to ensure that only high-quality examples train the NMT model. This selective training aims to enhance the model's ability to generate messages that are not only accurate but contextually relevant.

A critical aspect of the research was the identification of quality disparity in their generated messages. Some outputs were noted to be particularly strong, closely resembling manually written messages, while others lacked quality. To manage this variance, a quality-assurance filter was engineered. This filter is designed to detect scenarios where the model produces suboptimal messages, opting to issue a warning in such cases instead of an inadequate commit message.

Results and Implications

The paper's evaluation reveals a bifurcation in the quality of the generated commit messages, underscoring the importance of the quality-assurance mechanism. This finding emphasizes the potential for NMT to be a viable solution in certain contexts while highlighting the necessity for further refinement and error handling.

This research contributes both practically and theoretically to the field. Practically, it presents a method that can potentially save significant developer time and improve project documentation consistency. Theoretically, it underscores the complexities inherent in adapting NMT for tasks beyond traditional translation, such as understanding and summarizing code changes—a domain-dependent challenge requiring future exploration of more robust models and potentially hybrid approaches.

As neural machine translation and artificial intelligence continue to evolve, the findings from this paper could serve as a foundation for subsequent research into more sophisticated models capable of nuanced interpretations, ensuring high-quality outputs consistently. Future research might explore integrating additional contextual information, such as previous commit messages or project documentation, potentially enhancing the precision and utility of automated commit message generation.