RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data

Modern software development teams rely heavily on issue tracking systems such as JIRA and collaborative platforms like GitHub to manage feature requests, bug reports, and code changes. However, as projects scale, the volume of tickets, developer comments, and associated pull requests (PRs) grows exponentially, leading to information overload. Developers often spend significant time searching for past resolutions of similar issues, interpreting scattered conversations, and understanding linked code changes. Prior research has shown that machine learning techniques can aid in bug classification and triage [1], yet such approaches often fall short when handling the semantic variability in real-world bug reports. For instance, a bug describing "UI crash when toggling feature flags in React 19" might not be directly matched with an earlier issue phrased as "application freeze due to concurrent rendering," even though the root cause overlaps, reflecting broader challenges in applying traditional supervised methods to complex, evolving enterprise data [2]. To address this gap, we propose a Retrieval-Augmented Generation (RAG) framework that combines semantic retrieval with context-aware language models for ticket resolution. RAG has been shown to reduce hallucinations and improve factual accuracy by grounding model outputs in retrieved evidence [3], making it well-suited for enterprise contexts where precision and reliability are critical. Our system leverages Sentence-Transformers to create embeddings of JIRA tickets, user comments, and PR descriptions, FAISS (Facebook AI Similarity Search) to perform efficient approximate nearest neighbor (ANN) search across a large corpus of tickets and code metadata, and a Large Language Model (LLM) decoder that synthesizes retrieved evidence into grounded, context-rich resolution suggestions. This approach demonstrates how resolution latency can be reduced, organizational knowledge reuse improved, and duplicate engineering effort minimized, while incorporating linked PR information to provide actionable code-change insights that guided past fixes.