Paper 2601.11655
Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey
- Published
- Jan 2026
- Research lab
- Independent
- Citations
- 7
- GitHub
- 86 stars
01 In brief
Summary
This paper presents a systematic survey of LLM-based issue resolution in software engineering, a task formalized by benchmarks like SWE-bench.
The survey reviews 175 papers, organizing the field into a taxonomy covering data, methods, and analysis.
Data is categorized into evaluation and training datasets, with construction via automated collection or synthesis.
Methods are divided into training-free approaches (frameworks, tool and memory modules, inference-time scaling) and training-based approaches (SFT and RL).
Analysis covers data quality issues and agent behavior.
Key challenges include high computational overhead, lack of efficiency-aware evaluation, limited visual reasoning, safety risks, sparse rewards, data leakage, context management, insufficient patch validation, and limited universality.
The paper provides an open-source repository for ongoing resources.
02 From the paper
Abstract
Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment of benchmarks like SWE-bench revealed this task as profoundly difficult for large language models, thereby significantly accelerating the evolution of autonomous coding agents. This paper presents a systematic survey of this emerging domain. We begin by examining data construction pipelines, covering automated collection and synthesis approaches. We then provide a comprehensive analysis of methodologies, spanning training-free frameworks with their modular components to training-based techniques, including supervised fine-tuning and reinforcement learning. Subsequently, we discuss critical analyses of data quality and agent behavior, alongside practical applications. Finally, we identify key challenges and outline promising directions for future research. An open-source repository is maintained at https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution to serve as a dynamic resource in this field.