# Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing

arXiv: https://arxiv.org/abs/2606.08806 · HTML: https://mlsanity.com/paper/2606.08806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [AutoDev: Automated AI-Driven Development](https://mlsanity.com/paper/2403.08299/) (2024)
- [Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering](https://mlsanity.com/paper/2508.11824/) (2025)
- [Rethinking Software Engineering for Agentic AI Systems](https://mlsanity.com/paper/2604.10599/) (2026)
- [AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions](https://mlsanity.com/paper/2506.16586/) (2025)
- [AI-powered software testing tools: A systematic review and empirical assessment of their features and limitations](https://mlsanity.com/paper/2409.00411/) (2024)
- [Explainability as a Compliance Requirement: What Regulated Industries Need from AI Tools for Design Artifact Generation](https://mlsanity.com/paper/2507.09220/) (2025)
- [A Roadmap for Software Testing in Open Collaborative Development Environments](https://mlsanity.com/paper/2406.05438/) (2024)
- [How Students Use Generative AI for Software Testing: An Observational Study](https://mlsanity.com/paper/2510.10551/) (2025)

## Abstract (arXiv)

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 94.3% accuracy in governance, 96.5% artifact reliability, 94.2% compliance accuracy, and 90.8% explainability performance. The results show that autonomous testing systems that are governance-aware can significantly enhance the reliability, transparency, and operational security of autonomous testing systems in comparison to conventional AI-based testing systems. The proposed architecture is scalable and reliable and provides a safe environment for software testing.