{"ID":2856917,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10824","arxiv_id":"2510.10824","title":"Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration","abstract":"We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accuracy improvements from 65% to 94.8% while ensuring comprehensive document traceability throughout the quality engineering lifecycle. Experimental validation of enterprise Corporate Systems Engineering and SAP migration projects demonstrates an 85% reduction in testing timeline, an 85% improvement in test suite efficiency, and projected 35% cost savings, resulting in a 2-month acceleration of go-live.","short_abstract":"We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses tradit...","url_abs":"https://arxiv.org/abs/2510.10824","url_pdf":"https://arxiv.org/pdf/2510.10824v1","authors":"[\"Mohanakrishnan Hariharan\",\"Satish Arvapalli\",\"Seshu Barma\",\"Evangeline Sheela\"]","published":"2025-10-12T22:25:15Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}