LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
- URL: http://arxiv.org/abs/2508.12232v2
- Date: Tue, 02 Sep 2025 23:35:13 GMT
- Title: LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
- Authors: Arshia Akhavan, Alireza Hosseinpour, Abbas Heydarnoori, Mehdi Keshani,
- Abstract summary: A study on GitHub shows that only 42.2% of the issues are correctly linked to their commits.<n>We present LinkAnchor, the first autonomous LLM-based agent designed for issue-to-commit link recovery.
- Score: 1.5399429731150376
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Issue-to-commit link recovery plays an important role in software traceability and improves project management. However, it remains a challenging task. A study on GitHub shows that only 42.2% of the issues are correctly linked to their commits. This highlights the potential for further development and research in this area. Existing studies have employed various AI/ML-based approaches, and with the recent development of large language models, researchers have leveraged LLMs to tackle this problem. These approaches suffer from two main issues. First, LLMs are constrained by limited context windows and cannot ingest all of the available data sources, such as long commit histories, extensive issue comments, and large code repositories. Second, most methods operate on individual issue-commit pairs; that is, given a single issue-commit pair, they determine whether the commit resolves the issue. This quickly becomes impractical in real-world repositories containing tens of thousands of commits. To address these limitations, we present LinkAnchor, the first autonomous LLM-based agent designed for issue-to-commit link recovery. The lazy-access architecture of LinkAnchor enables the underlying LLM to access the rich context of software, spanning commits, issue comments, and code files, without exceeding the token limit by dynamically retrieving only the most relevant contextual data. Additionally, LinkAnchor is able to automatically pinpoint the target commit rather than exhaustively scoring every possible candidate. Our evaluations show that LinkAnchor outperforms state-of-the-art issue-to-commit link recovery approaches by 60-262% in Hit@1 score across all our case study projects. We also publicly release LinkAnchor as a ready-to-use tool, along with our replication package. LinkAnchor is designed and tested for GitHub and Jira, and is easily extendable to other platforms.
Related papers
- Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval [12.213080309713574]
Issue-commit linking, which connects issues with commits that fix them, is crucial for software maintenance.<n>We propose EasyLink, which utilizes a vector database as a modern Information Retrieval technique.<n>Under our evaluation, EasyLink achieves an average Precision@1 of 75.91%, improving over the state-of-the-art by over four times.
arXiv Detail & Related papers (2025-07-12T08:42:10Z) - SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
arXiv Detail & Related papers (2025-05-29T18:28:02Z) - RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving [9.477917878478188]
RepoMaster is an autonomous agent framework designed to explore and reuse GitHub repositories for solving complex tasks.<n>RepoMaster constructs function-call graphs, module-dependency graphs, and hierarchical code trees to identify essential components.<n>On our newly released GitTaskBench, RepoMaster lifts the task-pass rate from 24.1% to 62.9% while reducing token usage by 95%.
arXiv Detail & Related papers (2025-05-27T08:35:05Z) - SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering [74.04271300772155]
SyncMind is a framework that systematically defines the out-of-sync problem faced by large language model (LLM) agents in software engineering.<n>Based on SyncMind, we create SyncBench, a benchmark featuring 24,332 instances of agent out-of-sync scenarios in real-world CSE.
arXiv Detail & Related papers (2025-02-10T19:38:36Z) - SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution [56.9361004704428]
Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks.<n>SWE-Fixer is a novel open-source framework designed to effectively and efficiently resolve GitHub issues.<n>We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving competitive performance among open-source models.
arXiv Detail & Related papers (2025-01-09T07:54:24Z) - MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution [47.850418420195304]
Large Language Models (LLMs) have shown promise in code generation but face difficulties in resolving GitHub issues.
We propose a novel Multi-Agent framework for GitHub Issue reSolution, MAGIS, consisting of four agents customized for software evolution.
arXiv Detail & Related papers (2024-03-26T17:57:57Z) - ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks.
To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
arXiv Detail & Related papers (2023-11-16T12:03:21Z) - SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [80.52201658231895]
SWE-bench is an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories.
We show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues.
arXiv Detail & Related papers (2023-10-10T16:47:29Z) - Automated Recovery of Issue-Commit Links Leveraging Both Textual and
Non-textual Data [2.578242050187029]
Current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results.
We propose Hybrid-Linker to overcome such limitations by exploiting two information channels.
We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects.
arXiv Detail & Related papers (2021-07-05T09:38:44Z)
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.