In model-based testing, we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible.. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage is, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and also of probabilistic aggregate coverage in combination with k-wise coverage.

在基于模型的测试中，我们可能必须处理非确定性模型，例如，因为应用了抽象，或者因为被测软件本身是非确定性的。然后，根据软件做出的一些内部决策，同一测试用例可能会触发多个可能的执行路径。因此，无法进行精确的测试分析（例如计算测试覆盖率）。如果开发人员可以使用估计的概率为模型进行每次转换，则可以减轻这种情况。概率模型检查算法可随后用于进行简单的概率覆盖率分析。但是，在实践中，开发人员通常想知道所达到的总覆盖率是什么，不幸的是，不能将其重新表达为标准模型检查问题。k覆盖。