Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917207374561280 |
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| author | Li, Caihua Guo, Lianghong Wang, Yanlin Guo, Daya Tao, Wei Shan, Zhenyu Liu, Mingwei Chen, Jiachi Song, Haoyu Tang, Duyu Zhang, Hongyu Zheng, Zibin |
| author_facet | Li, Caihua Guo, Lianghong Wang, Yanlin Guo, Daya Tao, Wei Shan, Zhenyu Liu, Mingwei Chen, Jiachi Song, Haoyu Tang, Duyu Zhang, Hongyu Zheng, Zibin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11655 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey Li, Caihua Guo, Lianghong Wang, Yanlin Guo, Daya Tao, Wei Shan, Zhenyu Liu, Mingwei Chen, Jiachi Song, Haoyu Tang, Duyu Zhang, Hongyu Zheng, Zibin Software Engineering Computation and Language D.2.0; I.2.7 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. |
| title | Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey |
| topic | Software Engineering Computation and Language D.2.0; I.2.7 |
| url | https://arxiv.org/abs/2601.11655 |