Agentic Software Issue Resolution with Large Language Models: A Survey

Abstract

Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generative capabilities, LLM-based approaches have made significant progress in automated software issue resolution. However, real-world software issue resolution is inherently complex and requires long-horizon reasoning, iterative exploration, and feedback-driven decision-making that demand agentic capabilities beyond conventional single-step approaches. Recently, LLM-based agentic systems have become a promising research direction for software issue resolution, since the related literature has experienced explosive growth. Advancements in agentic software issue resolution can not only greatly enhance software maintenance efficiency and quality but also provide a realistic environment for validating agentic systems’ reasoning, planning, and execution capabilities, bridging AI and software engineering. This work presents a systematic survey of 242 recent studies at the forefront of LLM-based agentic software issue resolution research. It outlines the general workflow of the task and establishes a taxonomy across three dimensions: benchmarks, techniques, and empirical studies. Furthermore, it highlights how reinforcement learning has become an increasingly important training paradigm for agentic systems in software engineering. Finally, it summarizes key challenges and outlines promising directions for future research. The artifacts’ page accompanying this survey is at https://github.com/ZhonghaoJiang/Awesome-Issue-Solving.