Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration

• URL: http://arxiv.org/abs/2510.10824v1
• Date: Sun, 12 Oct 2025 22:25:15 GMT
• Title: Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
• Authors: Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela,
• Abstract summary: We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation.<n>We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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