AI 中文总结
本研究在HyTest基础上整合TReVa与FlaRe形成HyTestTF，可在CPS开发早期阶段仅一轮额外测试中用混合模型识别不稳定测试用例与条件，实证显示其能正确区分不稳定与非不稳定测试用例
AI 中文摘要
网络物理系统（CPS）有众多应用，从简单的恒温系统到自动驾驶系统和医疗设备不等。与所有系统一样，它们需要正确运行，为此需要采用测试等能有效揭露故障的验证技术。此外，CPS通常在不确定环境中运行，各种预期或意外事件会影响其行为，这些事件与定时及同步问题可能导致CPS出现不同行为，使CPS在某些条件下通过测试，而在其他条件下失败，这类测试用例被称为“不稳定测试用例”，导致这种情况的条件被称为“不稳定条件”，对应的行为被称为“不稳定行为”。当测试用例不稳定时，测试结果不可靠。为获得更可靠的测试结果，工程师可能会尝试识别并移除不稳定测试用例。本研究从我们先前创建的测试用例生成与执行技术HyTest出发，整合了两种新技术：一是用于验证HyTest提供的测试结果的TReVa，二是在CPS开发早期阶段仅需一轮额外测试即可使用混合模型识别不稳定测试用例和不稳定条件的FlaRe，我们将该新方法称为HyTestTF。我们呈现了评估该方法有效性的实证研究结果，结果表明其能正确区分不稳定测试用例与非不稳定测试用例
英文摘要
Cyber-Physical Systems (CPSs) have many applications, ranging from simple thermostat systems to autonomous driving systems and medical devices. Like all systems, they need to function correctly. To this end, validation techniques such as testing that can effectively reveal faults are required. Also, CPSs usually operate in uncertain environments where various expected/unexpected events can affect their behaviors. These events together with timing and synchronization incidents may result in various/different CPS behaviors and may cause a CPS to pass a test under some conditions and fail it under others. Such test cases are called ``flaky'' test cases and we call the conditions that are responsible for them ``flaky conditions'' and call these behaviors ``flaky behaviors''. When test cases are flaky, testing results are unreliable. To achieve more reliable test results, engineers may attempt to recognize flaky test cases and remove them. In this work, beginning with a test case generation and execution technique called HyTest that we created previously, we integrate TReVa, a new technique that validates testing results provided by HyTest and FlaRe, a new technique that recognizes flaky test cases and flaky conditions using hybrid models during the early stages of CPS development in just one additional round of testing. We present the results of an empirical study evaluating the effectiveness of our new approach (which we call HyTestTF). Our results show that correctly differentiate flaky test cases from non-flaky test cases
