LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
📰 ArXiv cs.AI
Learn how LinkAnchor, an autonomous LLM-based agent, recovers issue-to-commit links in software repositories, improving software traceability and project management
Action Steps
- Apply LLMs to issue-to-commit link recovery using LinkAnchor
- Configure the agent to integrate with GitHub repositories
- Test the accuracy of recovered links using evaluation metrics
- Run LinkAnchor on a sample dataset to demonstrate its effectiveness
- Compare the results with existing ML/DL approaches to assess improvements
Who Needs to Know This
Software engineers, project managers, and DevOps teams can benefit from LinkAnchor's automated link recovery, enhancing their workflow efficiency and accuracy
Key Insight
💡 Autonomous LLM-based agents like LinkAnchor can significantly improve issue-to-commit link recovery accuracy, enhancing software traceability and project management
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🚀 Introducing LinkAnchor: an autonomous LLM-based agent for issue-to-commit link recovery in software repositories! 📈
Key Takeaways
Learn how LinkAnchor, an autonomous LLM-based agent, recovers issue-to-commit links in software repositories, improving software traceability and project management
Full Article
Title: LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
Abstract:
arXiv:2508.12232v3 Announce Type: replace-cross Abstract: 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 proble
Abstract:
arXiv:2508.12232v3 Announce Type: replace-cross Abstract: 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 proble
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