Model transformations play an important role in the evolution of systems in various fields such as healthcare, automotive and aerospace industry. Thus, it is important to check the correctness of model transformation programs. Several approaches have been proposed to generate test cases for model transformations based on different coverage criteria (e.g., statements, rules, metamodel elements, etc.). However, the execution of a large number of test cases during the evolution of transformation programs is time-consuming and may include a lot of overlap between the test cases. In this paper, we propose a test case selection approach for model transformations based on multi-objective search. We use the non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-offs between two conflicting objectives: (1) maximize the coverage of rules and (2) minimize the execution time of the selected test cases. We validated our approach on several evolution cases of medium and large ATLAS Transformation Language programs.

中文翻译：

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模型转换的多标准测试用例选择

模型转换在医疗保健、汽车和航空航天工业等各个领域的系统演变中发挥着重要作用。因此，检查模型转换程序的正确性很重要。已经提出了几种方法来基于不同的覆盖标准（例如，语句、规则、元模型元素等）为模型转换生成测试用例。但是，在转换程序的演进过程中执行大量测试用例是耗时的，并且可能包括测试用例之间的大量重叠。在本文中，我们提出了一种基于多目标搜索的模型转换测试用例选择方法。我们使用非支配排序遗传算法 (NSGA-II) 来找到两个冲突目标之间的最佳权衡：(1) 最大化规则的覆盖范围和 (2) 最小化所选测试用例的执行时间。我们在大中型 ATLAS 转换语言程序的几个演化案例中验证了我们的方法。