Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
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 softwar...
| Authors | Dimple Bajaj, Deepak Khetan |
|---|---|
| Primary category | cs.SE |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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