Agentic Software Issue Resolution with Large Language Models: A Survey
Quick summary
arXiv:2512.22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generation, LLM-based approaches have made significant progress in automated software issue resolution. However, resolving real-world software issues is inherently complex and requires long-horizon reasoning, iterative exploration, and feedback-driven decision-making, w
Key takeaways
- arXiv:2512.22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.
- With the rapid development of large language models (LLMs) in reasoning and generation, LLM-based approaches have made significant progress in automated software issue resolution.
- However, resolving real-world software issues is inherently complex and requires long-horizon reasoning, iterative exploration, and feedback-driven decision-making, w
Why it matters
“Agentic Software Issue Resolution with Large Language Models: A Survey” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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