research area Methods and Algorithms, research area Tools, Platforms, Frameworksconference IEEE International Conference on Data and Software Engineering
content type paperpublished October 2025

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

AuthorsMohanakrishnan Hariharan, Seshu Babu Barma, Satish Arvapalli, Evangeline Sheela Arulanandam†

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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.

  • † Independent Researcher

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