论文标题
部分可观测时空混沌系统的无模型预测
Genetic Algorithms for Redundancy in Interaction Testing
论文作者
论文摘要
必须测试确定大型软件系统中的组件是否在功能上运行。相互作用测试涉及设计一套测试，该测试可以确保是否在少数组件之间存在一个相互作用的组件中存在故障。该测试的成本通常是由测试数量建模的，因此在减少此数字方面已经付出了很多努力。在这里，我们将冗余纳入模型，该模型允许在非确定性环境中进行测试。构造这些测试套件的现有算法通常涉及一种“快速”算法来生成大多数测试，而另一种“较慢”的算法以“完成”测试套件。我们采用了一种遗传算法，该算法概括了这些方法，该方法也通过增加所选算法的数量来融合冗余，我们称之为“阶段”。通过增加阶段的数量，我们表明，与现有技术相比，不仅可以减少测试数量，而且生成它们的计算时间也大大减少了。
It is imperative for testing to determine if the components within large-scale software systems operate functionally. Interaction testing involves designing a suite of tests, which guarantees to detect a fault if one exists among a small number of components interacting together. The cost of this testing is typically modeled by the number of tests, and thus much effort has been taken in reducing this number. Here, we incorporate redundancy into the model, which allows for testing in non-deterministic environments. Existing algorithms for constructing these test suites usually involve one "fast" algorithm for generating most of the tests, and another "slower" algorithm to "complete" the test suite. We employ a genetic algorithm that generalizes these approaches that also incorporates redundancy by increasing the number of algorithms chosen, which we call "stages." By increasing the number of stages, we show that not only can the number of tests be reduced compared to existing techniques, but the computational time in generating them is also greatly reduced.
