Governance Controls For AI-Generated Test Artifacts In Autonomous Software Testing

Abstract

Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly used in autonomous software testing; however, AI-generated test artifacts often suffer from hallucinations, compliance violations, security risks, and limited explainability. To enhance the reliability, transparency, and trustworthiness of AI-generated testing artifacts, this research introduces the concept of Governance-Aware Autonomous Testing Framework (GATF). The framework extends the autonomous testing lifecycle with governance validation, explainability analysis, probabilistic risk assessment, compliance monitoring, as well as audit governance. Experiments were performed with Defects4J and PROMISE software engineering datasets. The proposed framework successfully reduced the governance related risks by 89.6% and demonstrated with 94.3% accuracy in governance, 96.5% artifact reliability, 94.2% compliance accuracy and 90.8% explainability performance. The results show that the autonomous testing systems that are governance-aware can significantly enhance the reliability, transparency and operational security of the autonomous testing systems in comparison to the conventional AI-based testing systems. The architecture proposed is scalable and reliable and provides a safe environment for software testing.