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.

翻译：现代软件团队经常因知识分散在JIRA工单、开发者讨论和GitHub拉取请求（PR）中，而在解决重复或相关问题时遭遇延迟。为应对这一挑战，我们提出一种检索增强生成（RAG）框架，该框架集成Sentence-Transformers语义嵌入与基于FAISS的向量搜索，以提供上下文感知的工单解决建议。该方法通过嵌入历史JIRA工单、用户评论及关联的PR元数据来检索语义相似的过往案例，随后由大型语言模型（LLM）将其综合为基于证据且可解释的解决方案。本框架贡献在于：构建了连接JIRA与GitHub数据的统一流程、针对异构软件工件的嵌入与FAISS索引策略，以及基于检索证据的解决方案生成模块。通过精确率、召回率、解决时间缩减和开发者接受度等指标进行的实验评估表明，所提系统在现代DevOps环境中显著提升了解决准确率、修复质量和知识复用效率。