自主软件测试中AI生成测试工件的治理控制
Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
AI总结：
提出治理感知自主测试框架(GATF)，通过治理验证、可解释性分析、风险评估、合规监控和审计治理，将AI生成测试工件的治理风险降低89.6%，准确率达94.3%。
摘要：
人工智能（AI）和大语言模型（LLMs）越来越多地用于自主软件测试；然而，AI生成的测试工件常常存在幻觉、合规违规、安全风险和有限的可解释性。为了提高AI生成测试工件的可靠性、透明度和可信度，本研究引入了治理感知自主测试框架（GATF）的概念。该框架通过治理验证、可解释性分析、概率风险评估、合规监控以及审计治理来扩展自主测试生命周期。使用Defects4J和PROMISE软件工程数据集进行了实验。所提出的框架成功地将治理相关风险降低了89.6%，并在治理方面表现出94.3%的准确率、96.5%的工件可靠性、94.2%的合规准确率和90.8%的可解释性性能。结果表明，与传统的基于AI的测试系统相比，具有治理意识的自主测试系统可以显著提高自主测试系统的可靠性、透明度和操作安全性。所提出的架构具有可扩展性和可靠性，为软件测试提供了安全的环境。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
