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.

翻译：传统的缺陷追踪系统高度依赖人工报告、复现、分类与解决，涉及终端用户、客户支持、开发人员及测试人员等多方利益相关者。这种职责划分需要大量的协调与人力投入，扩大了非技术用户与开发者之间的沟通鸿沟，并显著延缓了从缺陷发现到部署的流程。此外，当前解决方案多为高度异步模式，常导致用户在获得反馈前需等待较长时间。本文系统考察了缺陷追踪实践的演进历程——从早期的纸质记录方法到现今的基于Web的平台，并提出一种前瞻性的AI驱动缺陷追踪框架愿景。该框架通过大语言模型（LLM）与智能体驱动的自动化增强现有系统；我们报告了其关键组件的早期适配案例，为其可行性提供了初步实证基础。所提出的框架旨在通过以下方式缩短解决时间并降低协调开销：允许终端用户以自然语言报告缺陷，同时由AI智能体完成报告精炼、尝试复现、缺陷分类、报告验证、建议无代码修复方案、生成补丁以及支持持续集成与部署。我们探讨了将LLM集成至缺陷追踪领域所面临的挑战与机遇，并展示了智能自动化如何将软件维护转变为更高效、协作且以用户为中心的过程。