This work was developed in an industrial setting towards UI regression testing, where we do not have access to source code and the majority of test cases are manually executed (and only part of the regression-based test cases can be executed due to limited resources). Test case prioritization (TCP) is indicated for such a scenario. But characteristic of many TCP techniques is that they rely on source code coverage information, whereas we just have access to test cases, change requests, and their features. Thus, our goal is to investigate which criteria is the most relevant for prioritization. Thus, according to the literature we create an optimization model based on historical data. This model is embedded in a constraint solver designed for optimization. Our optimization function is based on the APFD (Average of the Percentage of Faults Detected) metric, but other metrics can be used as well. We have found that our partner already uses an appropriate criterion to identify failures which is statistically equivalent to other criteria used in experiments using our optimization model.

这项工作是在面向UI回归测试的工业环境中开发的，在该环境中，我们无法访问源代码，并且大多数测试用例都是手动执行的（由于资源有限，只能执行部分基于回归的测试用例） 。针对这种情况指示了测试案例优先级（TCP）。但是许多TCP技术的特点是它们依赖于源代码覆盖率信息，而我们只能访问测试用例，更改请求及其功能。因此，我们的目标是研究与优先级最相关的标准。因此，根据文献，我们基于历史数据创建了一个优化模型。该模型嵌入为优化而设计的约束求解器中。我们的优化功能基于APFD（检测到的故障百分比的平均值）度量标准，但是也可以使用其他度量标准。我们发现我们的合作伙伴已经使用适当的准则来识别故障，这在统计学上等同于使用我们的优化模型进行实验的其他准则。