mlsanityData to 14 Oct 2026

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

arXiv 2510.08667 ↗First posted 9 Oct 2025cs.SE cs.AIcited by 2

On the map: Retrieval-augmented generation → RAG for code

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 18 references that are in the map. First posted on arXiv 9 Oct 2025.

Builds on

5 of 18 references.

References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2017Billion-scale similarity search with GPUscited by 2,497
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  5. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reportscited by 1
  2. 2026Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAGcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
  2. 2025A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
  3. 2026Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
  4. 2025What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
  5. 2025Repository-level Code Search with Neural Retrieval Methods
  6. 2025RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation
  7. 2025Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support
  8. 2025FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents