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.

翻译：问题解决作为现实世界开发中一项复杂的软件工程任务，已成为人工智能领域备受关注的挑战。SWE-bench等基准测试的建立表明，该任务对大型语言模型而言极具难度，从而显著加速了自主编码智能体的发展。本文对这一新兴领域进行了系统性综述。我们首先考察数据构建流程，涵盖自动化收集与合成方法。随后对方法论进行全面分析，包括基于免训练框架的模块化组件以及基于训练的技术，如监督微调与强化学习。接着，我们讨论数据质量与智能体行为的关键分析，以及实际应用场景。最后，我们指出该领域面临的核心挑战，并展望未来研究的潜在方向。本领域动态资源库持续维护于https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution。