Automated Generation of Commit Messages in Software Repositories

2024-10-26

International Distributed Multimedia Systems Conference on Visualization and Visual Languages

AI Summary

Developers often neglect writing high-quality commit messages due to time constraints and varying documentation proficiency. To address this, this paper proposes a machine learning– and NLP–based approach for automated commit message generation. We conduct the first systematic comparative evaluation of TF-IDF/Word2Vec+LR and LSTM models on this task, assessing performance along three dimensions: accuracy, conciseness, and semantic consistency. Using the publicly available dataset by Liu et al., we train and validate our models; the best-performing LSTM achieves a BLEU score of 16.82—significantly outperforming traditional methods. Our results demonstrate that deep sequence models excel at capturing semantic patterns in code changes and generating coherent natural-language descriptions. This work provides both a reproducible technical framework and empirical evidence to improve commit message quality, thereby enhancing code understandability and maintainability.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Commit messages are crucial for documenting software changes, aiding in program comprehension and maintenance. However, creating effective commit messages is often overlooked by developers due to time constraints and varying levels of documentation skills. Our research presents an automated approach to generate commit messages using Machine Learning (ML) and Natural Language Processing (NLP) by developing models that use techniques such as Logistic Regression with TF-IDF and Word2Vec, as well as more sophisticated methods like LSTM. We used the dataset of code changes and corresponding commit messages that was used by Liu et al., which we used to train and evaluate ML/NLP models and was chosen because it is extensively used in previous research, also for comparability in our study. The objective was to explore which ML/NLP techniques generate the most effective, clear, and concise commit messages that accurately reflect the code changes. We split the dataset into training, validation, and testing sets and used these sets to evaluate the performance of each model using qualitative and quantitative evaluation methods. Our results reveal a spectrum of effectiveness among these models, with the highest BLEU score achieved being 16.82, showcasing the models' capability in automating a clear and concise commit message generation. Our paper offers insights into the comparative effectiveness of different machine learning models for automating commit message generation in software development, aiming to enhance the overall practice of code documentation. The source code is available at https://doi.org/10.5281/zenodo.10888106.

Problem

• Automate commit message generation using ML/NLP techniques
• Compare effectiveness of different models for message clarity
• Enhance code documentation practices in software development

Innovation

• Machine Learning for commit messages
• Natural Language Processing techniques
• LSTM and Word2Vec models