Traditional bug tracking systems rely heavily on manual reporting, reproduction, triaging, and resolution, each carried out by different stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires significant coordination and widens the communication gap between non-technical users and technical teams, slowing the process from bug discovery to resolution. Moreover, current systems are highly asynchronous; users often wait hours or days for a first response, delaying fixes and contributing to frustration. This paper examines the evolution of bug tracking, from early paper-based reporting to today's web-based and SaaS platforms. Building on this trajectory, we propose an AI-powered bug tracking framework that augments existing tools with intelligent, large language model (LLM)-driven automation. Our framework addresses two main challenges: reducing time-to-fix and minimizing human overhead. Users report issues in natural language, while AI agents refine reports, attempt reproduction, and request missing details. Reports are then classified, invalid ones resolved through no-code fixes, and valid ones localized and assigned to developers. LLMs also generate candidate patches, with human oversight ensuring correctness. By integrating automation into each phase, our framework accelerates response times, improves collaboration, and strengthens software maintenance practices for a more efficient, user-centric future.

翻译：传统的缺陷追踪系统严重依赖人工报告、复现、分类与解决，每个环节由不同利益相关者（如终端用户、客户支持人员、开发人员与测试人员）分别执行。这种职责划分需要大量协调工作，并扩大了非技术用户与技术团队之间的沟通鸿沟，从而延缓了从缺陷发现到解决的进程。此外，当前系统具有高度异步性；用户通常需要等待数小时乃至数天才能获得首次响应，这既延迟了修复进度，也加剧了用户的不满情绪。本文系统考察了缺陷追踪技术的演进历程——从早期的纸质报告到当前的基于Web的SaaS平台。基于这一发展轨迹，我们提出一种由人工智能驱动的缺陷追踪框架，该框架通过智能化的、由大语言模型（LLM）驱动的自动化机制增强现有工具。我们的框架着力解决两大核心挑战：缩短修复周期与降低人工开销。用户可使用自然语言报告问题，而AI智能体则对报告进行精炼、尝试复现并主动索求缺失细节。随后报告将被分类处理：无效报告通过无代码修复方式解决，有效报告则进行问题定位并分配给开发人员。LLM还可生成候选补丁，并由人工审核确保其正确性。通过将自动化深度集成至每个阶段，本框架能够显著缩短响应时间、优化协作流程，并强化软件维护实践，从而构建更高效、以用户为中心的未来。