Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911206596411392 |
|---|---|
| author | Hariharan, Mohanakrishnan Arvapalli, Satish Barma, Seshu Sheela, Evangeline |
| author_facet | Hariharan, Mohanakrishnan Arvapalli, Satish Barma, Seshu Sheela, Evangeline |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10824 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration Hariharan, Mohanakrishnan Arvapalli, Satish Barma, Seshu Sheela, Evangeline Software Engineering Artificial Intelligence 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. |
| title | Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.10824 |