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LinkAnchor agent uses LLMs to improve software issue-to-commit link recovery

Researchers have developed LinkAnchor, an autonomous LLM-based agent designed to improve the recovery of issue-to-commit links in software development. This agent addresses limitations of previous methods, such as restricted context windows and the failure to account for complex commit chains. LinkAnchor employs a lazy-access architecture to dynamically retrieve only the most relevant contextual data, overcoming token limits and improving the accuracy of linking issues to their corresponding code changes. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel agent architecture for improving software traceability and project management through LLMs.

RANK_REASON This is a research paper detailing a new LLM-based agent for a specific software engineering task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Arshia Akhavan, Alireza Hosseinpour, Abbas Heydarnoori, Hamid Bagheri, Mehdi Keshani ·

    LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery

    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 cor…