论文标题
通过网络依赖性测试的实用假设推断，片状测试疗法化
Flaky Test Sanitisation via On-the-Fly Assumption Inference for Tests with Network Dependencies
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Flaky tests cause significant problems as they can interrupt automated build processes that rely on all tests succeeding and undermine the trustworthiness of tests. Numerous causes of test flakiness have been identified, and program analyses exist to detect such tests. Typically, these methods produce advice to developers on how to refactor tests in order to make test outcomes deterministic. We argue that one source of flakiness is the lack of assumptions that precisely describe under which circumstances a test is meaningful. We devise a sanitisation technique that can isolate f laky tests quickly by inferring such assumptions on-the-fly, allowing automated builds to proceed as flaky tests are ignored. We demonstrate this approach for Java and Groovy programs by implementing it as extensions for three popular testing frameworks (JUnit4, JUnit5 and Spock) that can transparently inject the inferred assumptions. If JUnit5 is used, those extensions can be deployed without refactoring project source code. We demonstrate and evaluate the utility of our approach using a set of six popular real-world programs, addressing known test flakiness issues in these programs caused by dependencies of tests on network availability. We find that our method effectively sanitises failures induced by network connectivity problems with high precision and recall.
