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

2025/10/12 by Hariharan, Mohanakrishnan, Arvapalli, Satish, Barma, Seshu +1 · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Software Engineering (cs.SE)

paper · doi:10.48550/arxiv.2510.10824

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.

Citations

• Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding
• Enhancing Software Quality Assurance with an Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model
• CareerX: A Retrieval-Augmented Generation Framework for Personalized AI-Driven Career Guidance
• Toolformer: Language Models Can Teach Themselves to Use Tools
• Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures
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• Dense Passage Retrieval for Open-Domain Question Answering
• An orchestrated survey of methodologies for automated software test case generation

Cited by

• Reinforcement Learning Integrated Agentic RAG for Software Test Cases Authoring

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