过去、现在与未来：生成AI时代的缺陷跟踪
Past, Present, and Future of Bug Tracking in the Generative AI Era
AI总结：
本文探讨生成AI时代缺陷跟踪的发展，提出基于大语言模型和智能代理的框架，旨在通过自然语言报告和AI自动化降低解决时间与协调成本。
摘要：
传统缺陷跟踪系统依赖手动报告、重现、分类和解决，涉及多个利益相关者，需要大量协调和人力，加剧非技术人员与开发者之间的沟通差距，并显著减缓从发现缺陷到部署的过程。此外，当前解决方案高度异步，常使用户长时间等待反馈。本文探讨了缺陷跟踪实践从早期纸质方法到当今网络平台的演变，并提出面向未来的AI驱动缺陷跟踪框架。该框架通过大语言模型（LLM）和代理驱动的自动化增强现有系统，并报告其关键组件的早期应用，提供初步实证基础。所提框架旨在通过允许终端用户用自然语言报告缺陷，同时AI代理优化报告、尝试重现、分类缺陷、验证报告、建议无代码修复、生成补丁并支持持续集成和部署，从而减少解决时间和协调开销。本文讨论了将LLM整合到缺陷跟踪中的挑战和机遇，并展示智能自动化如何将软件维护转变为更高效、协作和以用户为中心的过程。
英文摘要：
Traditional bug-tracking systems rely heavily on manual reporting, reproduction, classification, and resolution, involving multiple stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires substantial coordination and human effort, widens the communication gap between non-technical users and developers, and significantly slows the process from bug discovery to deployment. Moreover, current solutions are highly asynchronous, often leaving users waiting long periods before receiving any feedback. In this paper, we examine the evolution of bug-tracking practices, from early paper-based methods to today's web-based platforms, and present a forward-looking vision of an AI-powered bug tracking framework. The framework augments existing systems with large language model (LLM) and agent-driven automation, and we report early adaptations of its key components, providing initial empirical grounding for its feasibility. The proposed framework aims to reduce time to resolution and coordination overhead by enabling end users to report bugs in natural language while AI agents refine reports, attempt reproduction, classify bugs, validate reports, suggest no-code fixes, generate patches, and support continuous integration and deployment. We discuss the challenges and opportunities of integrating LLMs into bug tracking and show how intelligent automation can transform software maintenance into a more efficient, collaborative, and user-centric process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
