用AI整合JIRA和GitHub数据，自动推荐问题解决方法。
RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
- 用语义嵌入+向量检索，从历史工单中找相似案例。
- 实验显示修复准确率提升，解决时间缩短，开发者接受度高。
- 适合开发团队快速复用知识，提升运维效率。
现代软件团队常因分散在JIRA工单、开发讨论和GitHub Pull Request中的知识碎片化，导致重复或关联问题处理延迟。为此，我们提出一种融合句子嵌入与基于FAISS的向量搜索的检索增强生成（RAG）框架，将历史JIRA工单、用户评论及关联的PR元数据进行语义编码，检索出语义相似的过往案例，并由大语言模型（LLM）生成有依据且可解释的修复建议。该框架构建了连接JIRA与GitHub数据的统一流程，设计了针对异构软件资产的嵌入与FAISS索引策略，以及基于检索证据的生成模块。通过精确率、召回率、修复时间缩减及开发者接受度等指标评估，结果表明该系统显著提升了修复准确性、修复质量与知识复用率，在现代DevOps环境中表现优异。
原文摘要 · Abstract (English)
Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with FAISS-based vector search to deliver context-aware ticket resolution recommendations. The approach embeds historical JIRA tickets, user comments, and linked PR metadata to retrieve semantically similar past cases, which are then synthesized by a Large Language Model (LLM) into grounded and explainable resolution suggestions. The framework contributes a unified pipeline linking JIRA and GitHub data, an embedding and FAISS indexing strategy for heterogeneous software artifacts, and a resolution generation module guided by retrieved evidence. Experimental evaluation using precision, recall, resolution time reduction, and developer acceptance metrics shows that the proposed system significantly improves resolution accuracy, fix quality, and knowledge reuse in modern DevOps environments.
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