Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
- URL: http://arxiv.org/abs/2404.14824v1
- Date: Tue, 23 Apr 2024 08:24:43 GMT
- Title: Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
- Authors: Pengyu Xue, Linhao Wu, Zhongxing Yu, Zhi Jin, Zhen Yang, Xinyi Li, Zhenyu Yang, Yue Tan,
- Abstract summary: Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs.
This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
- Score: 24.151927600694066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have demonstrated extensive applicability in diverse code-related task. But few studies systematically explored their effectiveness using LLMs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying LLM to generate high-quality commit messages. Motivated by a pilot analysis, we first clean the most widely-used CMG dataset following practitioners' criteria. Afterward, we re-evaluate diverse state-of-the-art CMG approaches and make comparisons with LLMs, demonstrating the superior performance of LLMs against state-of-the-art CMG approaches. Then, we further propose four manual metrics following the practice of OSS, including Accuracy, Integrity, Applicability, and Readability, and assess various LLMs accordingly. Results reveal that GPT-3.5 performs best overall, but different LLMs carry different advantages. To further boost LLMs' performance in the CMG task, we propose an Efficient Retrieval-based In-Context Learning (ICL) framework, namely ERICommiter, which leverages a two-step filtering to accelerate the retrieval efficiency and introduces semantic/lexical-based retrieval algorithm to construct the ICL examples. Extensive experiments demonstrate the substantial performance improvement of ERICommiter on various LLMs for code diffs of different programming languages. Meanwhile, ERICommiter also significantly reduces the retrieval time while keeping almost the same performance. Our research contributes to the understanding of LLMs' capabilities in the CMG field and provides valuable insights for practitioners seeking to leverage these tools in their workflows.
Related papers
- Evaluating Linguistic Capabilities of Multimodal LLMs in the Lens of Few-Shot Learning [15.919493497867567]
This study aims to evaluate the performance of Multimodal Large Language Models (MLLMs) on the VALSE benchmark.
We conducted a comprehensive assessment of state-of-the-art MLLMs, varying in model size and pretraining datasets.
arXiv Detail & Related papers (2024-07-17T11:26:47Z) - SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts.
We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM.
We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
arXiv Detail & Related papers (2024-07-16T04:41:58Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions [2.5179515260542544]
Large Language Models (LLMs) have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization.
To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics.
This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use.
arXiv Detail & Related papers (2024-04-14T03:54:00Z) - FAC$^2$E: Better Understanding Large Language Model Capabilities by
Dissociating Language and Cognition [57.747888532651]
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks.
We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
arXiv Detail & Related papers (2024-02-29T21:05:37Z) - If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code
Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
Large language models (LLMs) are trained on a combination of natural language and formal language (code)
Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
arXiv Detail & Related papers (2024-01-01T16:51:20Z) - MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language
Feedback [78.60644407028022]
We introduce MINT, a benchmark that evaluates large language models' ability to solve tasks with multi-turn interactions.
LLMs generally benefit from tools and language feedback, with performance gains of 1-8% for each turn of tool use.
LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities.
arXiv Detail & Related papers (2023-09-19T15:25:42Z) - LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated.
We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization.
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
arXiv Detail & Related papers (2023-08-23T16:32:54Z) - Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation
with Large Language Models [12.708117108874083]
Large Language Models (LLMs) generate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning.
Previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples.
In this paper, we deliver a comprehensive study of.
PEFT techniques for LLMs under the automated code generation scenario.
arXiv Detail & Related papers (2023-08-21T04:31:06Z) - Through the Lens of Core Competency: Survey on Evaluation of Large
Language Models [27.271533306818732]
Large language model (LLM) has excellent performance and wide practical uses.
Existing evaluation tasks are difficult to keep up with the wide range of applications in real-world scenarios.
We summarize 4 core competencies of LLM, including reasoning, knowledge, reliability, and safety.
Under this competency architecture, similar tasks are combined to reflect corresponding ability, while new tasks can also be easily added into the system.
arXiv Detail & Related papers (2023-08-15T17:40:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.