面向类人自动测试生成：认知与问题解决的视角
Towards Human-Like Automated Test Generation: Perspectives from Cognition and Problem Solving
摘要：
自动化测试工具通常创建与人类测试人员创建的测试用例不同的测试用例。这通常会使工具效率降低，创建的测试更难理解，从而导致工具对人类测试人员的支持减少。在这里，我们提出了一个基于认知科学的框架，特别是分析解决问题的方法，识别测试人员的认知过程。该框架有助于映射人工测试活动中使用的测试设计步骤和标准，从而更好地理解有效的人工测试人员如何执行他们的任务。最终，我们的目标是能够模仿人类如何创建测试用例，从而设计出更像人类的自动化测试生成系统。我们认为，这样的系统可以更好地增强和支持测试人员的方式是有意义的。
英文摘要：
Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem-solving, for identifying cognitive processes of testers. The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks. Ultimately, our goal is to be able to mimic how humans create test cases and thus to design more human-like automated test generation systems. We posit that such systems can better augment and support testers in a way that is meaningful to them.
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。
