# LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery arXiv: https://arxiv.org/abs/2508.12232 ยท HTML: https://mlsanity.com/paper/2508.12232/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 31 of its 90 references that are in the map. - First posted on arXiv 17 Aug 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 30 of 31) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024) - [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019) - [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024) - [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024) - [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024) - [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024) - [LocAgent: Graph-Guided LLM Agents for Code Localization](https://mlsanity.com/paper/2503.09089/) (2025) - [Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration](https://mlsanity.com/paper/2406.01422/) (2024) - [SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution](https://mlsanity.com/paper/2507.23348/) (2025) - [A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization](https://mlsanity.com/paper/2308.05487/) (2023) - [You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects](https://mlsanity.com/paper/2412.10133/) (2024) - [Semantically Enhanced Software Traceability Using Deep Learning Techniques](https://mlsanity.com/paper/1804.02438/) (2018) - [Opportunities and Challenges in Code Search Tools](https://mlsanity.com/paper/2011.02297/) (2020) - [Agentic Bug Reproduction for Effective Automated Program Repair at Google](https://mlsanity.com/paper/2502.01821/) (2025) - [Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Models](https://mlsanity.com/paper/2102.04411/) (2021) - [Agentic AI Software Engineers: Programming with Trust](https://mlsanity.com/paper/2502.13767/) (2025) - [Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability](https://mlsanity.com/paper/2308.00229/) (2023) - [VulCurator: A Vulnerability-Fixing Commit Detector](https://mlsanity.com/paper/2209.03260/) (2022) - [EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery](https://mlsanity.com/paper/2308.10759/) (2023) - [Automatic Traceability Maintenance via Machine Learning Classification](https://mlsanity.com/paper/1807.06684/) (2018) - [Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks](https://mlsanity.com/paper/2005.09046/) (2020) - [Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation](https://mlsanity.com/paper/2302.07445/) (2023) - [Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data](https://mlsanity.com/paper/2107.01894/) (2021) - [Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval](https://mlsanity.com/paper/2507.09199/) (2025) - [Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery](https://mlsanity.com/paper/2209.01734/) (2022) - [Can GitHub Issues Help in App Review Classifications?](https://mlsanity.com/paper/2308.14211/) (2023) ## Built on by (most cited first, 2 of 2) - [LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment](https://mlsanity.com/paper/2601.01780/) (2026) - [Demystifying Agent Skills for Smart Contract Auditing: Design, Effectiveness, Behavioral Impact](https://mlsanity.com/paper/2609.29454/) (2026) ## Abstract (arXiv) Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challenging task. Prior studies show that only about 42.2% of issues on GitHub are correctly linked to their commits, highlighting the need for more effective solutions. Existing work has explored a range of ML/DL approaches, and more recently, large language models (LLMs) have been applied to this problem. However, these methods face two major limitations. First, LLMs are restricted by limited context windows and cannot simultaneously process all available data sources, such as long commit histories, extensive issue discussions, and large code repositories. Second, most approaches operate on individual issue-commit pairs, where a model independently scores the relevance of a single commit to an issue. This pairwise formulation fails to account for the complex associativity of software fixes, where an issue is often resolved by an aggregate chain of commits rather than a single atomic change. By ignoring these temporal and parental dependencies, existing methods often fail to incorporate the complete resolution logic and might misidentify intermediate commits as final fixes. Furthermore, this strategy is computationally inefficient in large repositories, as it requires exhaustively evaluating an enormous number of candidate pairs. To address these challenges, we present LinkAnchor, the first autonomous LLM-based agent designed specifically for issue-to-commit link recovery. LinkAnchor introduces a lazy-access architecture that allows the underlying LLM to dynamically retrieve only the most relevant contextual data, such as commits, issue comments, and code files, without exceeding token limits.