一句话：通过LLM增强issue报告、刻画开发者工作流并自动化UI缺陷定位与方案识别，推进AI驱动的issue解决。
有效的 issue 解决对维持软件质量至关重要，但开发者常受困于低质量 issue 报告、对真实工作流理解不足以及自动化支持缺乏。该研究从三个互补方向推进：一是利用 LLM 推理与应用特定信息增强 issue 报告；二是对传统系统和 AI 增强系统中的开发者工作流进行实证刻画；三是通过 ML、DL 与 LLM 方法自动化认知负荷较高的解决任务，包括缺陷 UI 定位和解决方案识别。整体工作旨在提供实证洞察、实用工具与自动化方法，推动 AI 驱动的 issue 解决，并支持更可维护、高质量的软件系统。
Effective issue resolution is crucial for maintaining software quality. Yet developers frequently encounter challenges such as low-quality issue reports, limited understanding of real-world workflows, and a lack of automated support. This research aims to address these challenges through three complementary directions. First, we enhance the issue report by proposing techniques that leverage LLM reasoning and application-specific information. Second, we empirically characterize developer workflows in both traditional and AI-augmented systems. Third, we automate cognitively demanding resolution tasks, including buggy UI localization and solution identification, through ML, DL, and LLM-based approaches. Together, our work delivers empirical insights, practical tools, and automated methods to advance AI-driven issue resolution, supporting more maintainable and high-quality software systems.