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.

中文翻译：

---

交互测试中冗余的遗传算法

必须进行测试以确定大型软件系统中的组件是否正常运行。交互测试涉及设计一套测试，如果一个错误存在于一起交互的少数组件中，它可以保证检测到错误。这种测试的成本通常以测试数量为模型，因此在减少这个数量方面付出了很多努力。在这里，我们将冗余纳入模型，允许在非确定性环境中进行测试。用于构建这些测试套件的现有算法通常涉及一种用于生成大部分测试的“快速”算法，以及用于“完成”测试套件的另一种“较慢”算法。我们采用了一种遗传算法来概括这些方法，该算法还通过增加所选算法的数量来合并冗余，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。