人工智能辅助软件测试中对测试代理的（过度）依赖
(Over)Reliance on Test Agents in AI-Assisted Software Testing
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中文总结 AI 辅助
探讨人工智能辅助软件测试中对测试代理的过度依赖问题，通过软件测试作为认知问题解决等三个理论视角阐述，提出收集过度依赖数据的框架及识别模式，旨在支持加速测试且不削弱测试判断与证据价值。
中文摘要 AI 辅助
基于人工智能的测试代理有望通过缩短持续开发中的反馈循环以及提高可扩展性和可维护性来加速软件测试。为实现这些益处，工程师仍须能够评估代理输出是否有用、有效和可靠，而非因其来自功能强大的系统就视其可信。本文认为，测试中对人工智能的过度依赖既是一种代理问题，即工程师可能在测试设计决策上放弃认知控制，也是一种保证问题，即测试工件可能未经充分审查就被当作证据接受。我们通过软件测试作为认知问题解决、测试代理作为自适应自主实体、测试设计论证作为使生成的测试可审查的手段这三个理论视角来阐述此观点。我们提出一个收集测试代理工作流程中过度依赖数据的框架，并识别出过度依赖的具体模式。目标是在不削弱判断或测试证据保证价值的情况下支持加速测试。
英文摘要
AI-based test agents promise to accelerate software testing by shortening feedback loops in continuous development and improving scalability and maintainability. To realize these benefits, engineers must still be able to assess if agent outputs are useful, valid, and reliable, rather than treating them as credible because they come from a capable system. This paper argues that overreliance on AI in testing is both an agency problem, in which engineers may cede cognitive control over test design decisions, and an assurance problem, in which testing artifacts may be accepted as evidence without sufficient scrutiny. We develop this argument through three theoretical lenses: software testing as cognitive problem-solving, test agents as adaptively autonomous entities, and test design argumentation as a means of making generated tests reviewable. We propose a framework for collecting data on overreliance in test agent workflows and identify specific modes of overdependence. The goal is to support accelerated testing without weakening judgment or the assurance value of testing evidence.
