Sampling techniques, such as t-wise interaction sampling are used to enable efficient testing for configurable systems. This is achieved by generating a small yet representative sample of configurations for a system, which circumvents testing the entire solution space. However, by design, most recent approaches for t-wise interaction sampling only consider combinations of configuration options from a configurable system's variability model and do not take into account their mapping onto the solution space, thus potentially leaving critical implementation artifacts untested. Tartler et al. address this problem by considering presence conditions of implementation artifacts rather than pure configuration options, but do not consider the possible interactions between these artifacts. In this paper, we introduce t-wise presence condition coverage, which extends the approach of Tartler et al. by using presence conditions extracted from the code as basis to cover t-wise interactions. This ensures that all t-wise interactions of implementation artifacts are included in the sample and that the chance of detecting combinations of faulty configuration options is increased. We evaluate our approach in terms of testing efficiency and testing effectiveness by comparing the approach to existing t-wise interaction sampling techniques. We show that t-wise presence condition sampling is able to produce mostly smaller samples compared to t-wise interaction sampling, while guaranteeing a t-wise presence condition coverage of 100%.
翻译:取样技术,例如Twitter互动取样技术,用于对可配置系统进行有效测试。这是通过为一个绕过整个解决方案空间测试的系统生成一个小型但有代表性的配置样本来实现的。然而,在设计上,最新的Twitter互动取样方法仅考虑从可配置系统变异模型中组合各种配置选项的组合,而不考虑在解决方案空间上进行绘图,从而有可能使关键的执行工艺品得不到测试。塔尔特勒等人通过考虑执行工艺品的存在条件而不是纯配置选项来解决这一问题,但不考虑这些工艺品之间的可能互动。在本文件中,我们采用了 " 临时存在条件 " 覆盖,扩大了Tartler等人的做法,其方法是利用从代码中提取的存在条件作为基础来覆盖高端互动。这确保了实施工艺品在样本中包含所有多端互动,并增加了检测错误配置选项组合的可能性。我们从测试效率和测试有效性的角度,将现有方法与现有100 %的抽样测试方法进行比较。我们展示了一种条件,从而能够对存在进行更精确的取样技术进行比较。我们展示了一种条件。