arXiv:2606.08806v1 Announce Type: cross 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 analysi
The rapid deployment of AI and LLMs in software development has exposed critical reliability, compliance, and security issues that necessitate immediate governance frameworks.
This research addresses the growing need for control and trustworthiness in AI-generated software artifacts, which is crucial for widespread enterprise adoption and regulatory acceptance.
The introduction of the Governance-Aware Autonomous Testing Framework (GATF) changes how AI-driven software testing will be validated and explained, extending the autonomous testing lifecycle with governance validation.
- · AI/LLM developers
- · Software testing companies
- · DevOps teams
- · Regulatory bodies
- · Software developers relying solely on unvalidated AI outputs
- · Companies with poor governance practices
- · Manual testing organizations slow to adapt
Increased adoption of governance frameworks for AI in critical software development.
Development of industry standards and certifications for AI-generated test artifacts.
Enhanced trust in AI-driven automation leading to faster software release cycles and more complex AI integration.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI