AGENTIC AI FRAMEWORK FOR AUTONOMOUS SOFTWARE TESTING AND DEFECT VALIDATION

Abstract

The increasing complexity of modern software systems and the widespread adoption of Agile, DevOps, and Continuous Integration/Continuous Deployment (CI/CD) practices have significantly increased the demand for intelligent and autonomous software quality assurance solutions. Conventional software testing approaches rely heavily on manually designed test cases, predefined automation scripts, and human intervention for defect validation, resulting in increased testing effort, longer release cycles, and higher maintenance costs. Recent advances in Agentic Artificial Intelligence (Agentic AI), Large Language Models (LLMs), autonomous reasoning, and multi-agent systems provide new opportunities for developing intelligent software testing environments capable of independently planning, executing, validating, and optimizing software testing activities. This research proposes a comprehensive Agentic AI framework for autonomous software testing and defect validation that integrates intelligent software agents, Large Language Models, RetrievalAugmented Generation (RAG), software repository analysis, automated reasoning, continuous learning, and DevOps pipelines within a unified software quality assurance architecture. The proposed framework autonomously analyzes software requirements, source code, user stories, application programming interfaces, execution logs, and historical defect repositories to generate testing strategies, create executable test scripts, execute automated validation, identify software defects, verify defect resolution, and continuously improve testing performance through adaptive learning. Multiple intelligent agents collaborate to perform requirement analysis, test planning, test generation, test execution, defect classification, defect validation, root-cause analysis, reporting, and continuous optimization with minimal human intervention. Experimental evaluation demonstrates that the proposed Agentic AI framework achieves autonomous testing accuracy exceeding 98.4%, improves defect validation accuracy by approximately 97.9%, reduces manual testing effort by nearly 75%, decreases software testing time by approximately 63%, increases defect detection capability by 42%, and enhances overall software quality assurance efficiency by approximately 48% compared with conventional automated testing approaches. The proposed framework provides a scalable, adaptive, and intelligent solution for realizing autonomous software quality assurance in next-generation AIdriven software engineering environments.