Comprehensive Evaluation of Large Language Models for Automated Commit Message Generation: Insights and Improvements

Core Concepts

Large Language Models demonstrate superior performance compared to state-of-the-art commit message generation approaches, with GPT-3.5 leading among LLMs. However, LLMs tend to convey more about "What" information but less detailed concerning "Why" information due to limited code context.

Abstract

The paper conducts a systematic empirical study on the performance of diverse Large Language Models (LLMs) for automated commit message generation (CMG) tasks. It first cleans up the widely-used MCMD dataset to ensure high-quality samples containing both "what" (specific code changes) and "why" (reasons behind changes) information.
The study then re-evaluates a wide range of state-of-the-art CMG approaches against recent LLMs, including GPT-3.5, LLaMA, and Gemini, using both automated and manual evaluation metrics. The results show that LLMs, especially GPT-3.5, demonstrate superior performance compared to previous CMG approaches. GPT-3.5 outperforms the best-performing CMG approach, RACE, by 83.85% and 27.20% in METEOR and BLEU metrics, respectively.
The manual assessment further reveals that GPT-3.5 performs the best overall, excelling in aspects like Accuracy, Integrity (covering "What" and "Why"), Applicability, and Readability. However, LLMs tend to convey more about "What" information but less detailed concerning "Why" information, likely due to the limited available code context.
To further boost LLMs' performance in CMG, the paper proposes an Efficient Retrieval-based In-Context Learning (ICL) framework, ERICommiter, which leverages two-step filtering and semantic/lexical-based retrieval to construct high-quality ICL examples. Extensive experiments demonstrate substantial performance improvements of ERICommiter on various LLMs for code diffs across different programming languages, while also significantly reducing the retrieval time.

Stats

GPT-3.5 outperforms the best-performing CMG approach, RACE, by 83.85% and 27.20% in METEOR and BLEU metrics, respectively.
In manual assessment, GPT-3.5 achieves an average score of 2.68 in Accuracy, outperforming other LLMs (1.85, 1.88, 1.63).
GPT-3.5 scores 2.56 and 2.18 in the "What" and "Why" aspects of Integrity, respectively, higher than other LLMs.

Quotes

"Large Language Models demonstrate superior performance compared to state-of-the-art commit message generation approaches, with GPT-3.5 leading among LLMs."
"LLMs tend to convey more about 'What' information but less detailed concerning 'Why' information due to limited code context."

Key Insights Distilled From

Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond

by Pengyu Xue,L... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14824.pdf

Deeper Inquiries

How can we further improve LLMs' ability to capture the "Why" information in commit messages, beyond the code context?

To enhance LLMs' capability in capturing the "Why" information in commit messages beyond the code context, several strategies can be implemented:

Context Expansion: LLMs can benefit from a broader context understanding by incorporating additional information sources such as commit history, issue tracking systems, and project documentation. By analyzing the historical context of code changes and related discussions, LLMs can better infer the reasons behind specific modifications.

Semantic Enrichment: Introducing semantic enrichment techniques like entity recognition, sentiment analysis, and topic modeling can help LLMs grasp the underlying motivations behind code changes. By identifying key entities, sentiments, and topics in the code diffs, LLMs can generate more informative and contextually rich commit messages.

Knowledge Graph Integration: Integrating a knowledge graph that captures domain-specific knowledge and relationships can provide LLMs with a structured representation of the software project. By leveraging the knowledge graph, LLMs can infer the rationale behind code changes based on the interconnectedness of project components and concepts.

Interactive Learning: Implementing interactive learning mechanisms where developers can provide feedback on the generated commit messages can help LLMs refine their understanding of the "Why" information. By incorporating human feedback iteratively, LLMs can learn to generate more accurate and contextually relevant commit messages.

Multi-Modal Fusion: Leveraging multi-modal fusion techniques that combine textual information from code differentials with visual representations (e.g., code snippets, diagrams) can offer a more comprehensive understanding of the code changes. By integrating multiple modalities, LLMs can capture a holistic view of the code modifications and their underlying reasons.

What other techniques, beyond in-context learning, could be explored to enhance LLMs' performance in the commit message generation task?

In addition to in-context learning, several other techniques can be explored to enhance LLMs' performance in commit message generation:

Transfer Learning: Utilizing transfer learning approaches where LLMs are pre-trained on a large corpus of code-related text data before fine-tuning on commit message generation tasks can improve their performance. By leveraging knowledge from pre-training, LLMs can better understand code semantics and generate more accurate commit messages.

Domain-Specific Fine-Tuning: Fine-tuning LLMs on domain-specific datasets related to software engineering can enhance their understanding of code-related contexts and terminology. By tailoring the model to the software development domain, LLMs can generate more relevant and precise commit messages.

Ensemble Learning: Employing ensemble learning techniques that combine predictions from multiple LLMs or CMG approaches can lead to more robust and accurate commit message generation. By aggregating diverse models' outputs, ensemble methods can mitigate individual model biases and improve overall performance.

Adversarial Training: Implementing adversarial training methods to enhance the robustness and generalization of LLMs in commit message generation. By exposing the model to adversarial examples during training, LLMs can learn to generate more resilient and contextually appropriate commit messages.

Explainable AI Techniques: Integrating explainable AI techniques that provide insights into the model's decision-making process can enhance transparency and trust in LLM-generated commit messages. By enabling developers to understand how LLMs arrive at their predictions, they can better interpret and validate the generated commit messages.

How can the insights from this study on LLMs' capabilities in commit message generation be applied to improve developer productivity and collaboration in other software engineering tasks?

The insights from the study on LLMs' capabilities in commit message generation can be leveraged to enhance developer productivity and collaboration in various software engineering tasks:

Automated Documentation Generation: By applying LLMs to automatically generate documentation for code changes, developers can save time on manual documentation tasks and ensure consistent and informative commit messages. This can streamline the development process and improve knowledge sharing among team members.

Code Review Assistance: Integrating LLMs into code review processes to provide contextual explanations and summaries of code changes can facilitate more effective code reviews. Developers can quickly grasp the purpose and impact of code modifications, leading to faster and more insightful code reviews.

Bug Report Summarization: Using LLMs to summarize bug reports and link them to corresponding code changes can help developers prioritize and address issues more efficiently. By automatically generating informative summaries, LLMs can assist in triaging and resolving bugs promptly.

Natural Language Interfaces: Developing natural language interfaces powered by LLMs to interact with software systems can enhance developer collaboration and communication. By enabling developers to query and retrieve information using natural language, LLMs can simplify complex tasks and foster better teamwork.

Knowledge Base Construction: Employing LLMs to construct and maintain knowledge bases of software projects can centralize information and insights for developers. By continuously updating and expanding the knowledge base with LLM-generated content, developers can access valuable resources for problem-solving and decision-making.

Overall, the application of LLMs in various software engineering tasks based on the insights gained from commit message generation can lead to improved efficiency, collaboration, and quality in software development processes.