Regression test selection (RTS) approaches reduce the cost of regression testing of evolving software systems. Existing RTS approaches based on UML models use behavioral diagrams or a combination of structural and behavioral diagrams. However, in practice, behavioral diagrams are incomplete or not used. In previous work, we proposed a fuzzy logic based RTS approach called FLiRTS that uses UML sequence and activity diagrams. In this work, we introduce FLiRTS 2, which drops the need for behavioral diagrams and relies on system models that only use UML class diagrams, which are the most widely used UML diagrams in practice. FLiRTS 2 addresses the unavailability of behavioral diagrams by classifying test cases using fuzzy logic after analyzing the information commonly provided in class diagrams. We evaluated FLiRTS 2 on UML class diagrams extracted from 3331 revisions of 13 open-source software systems, and compared the results with those of code-based dynamic (Ekstazi) and static (STARTS) RTS approaches. The average test suite reduction using FLiRTS 2 was 82.06%. The average safety violations of FLiRTS 2 with respect to Ekstazi and STARTS were 18.88% and 16.53%, respectively. FLiRTS 2 selected on average about 82% of the test cases that were selected by Ekstazi and STARTS. The average precision violations of FLiRTS 2 with respect to Ekstazi and STARTS were 13.27% and 9.01%, respectively. The average mutation score of the full test suites was 18.90%; the standard deviation of the reduced test suites from the average deviation of the mutation score for each subject was 1.78% for FLiRTS 2, 1.11% for Ekstazi, and 1.43% for STARTS. Our experiment demonstrated that the performance of FLiRTS 2 is close to the state-of-art tools for code-based RTS but requires less information and performs the selection in less time.

回归测试选择 (RTS) 方法降低了对不断发展的软件系统进行回归测试的成本。现有的基于 UML 模型的 RTS 方法使用行为图或结构图和行为图的组合。然而，在实践中，行为图不完整或没有使用。在之前的工作中，我们提出了一种基于模糊逻辑的 RTS 方法，称为 FLiRTS，它使用 UML 序列和活动图。在这项工作中，我们引入了 FLiRTS 2，它不再需要行为图并依赖于仅使用 UML 类图，这是实践中使用最广泛的 UML 图。FLiRTS 2 通过在分析类图中通常提供的信息后使用模糊逻辑对测试用例进行分类来解决行为图的不可用性问题。我们在从 13 个开源软件系统的 3331 个修订版中提取的 UML 类图上评估了 FLiRTS 2，并将结果与​​基于代码的动态 (Ekstazi) 和静态 (STARTS) RTS 方法的结果进行了比较。使用 FLiRTS 2 的平均测试套件减少率为 82.06%。FLiRTS 2 在 Ekstazi 和 STARTS 方面的平均安全违规率分别为 18.88% 和 16.53%。FLiRTS 2 平均选择了 Ekstazi 和 STARTS 选择的测试用例的 82%。FLiRTS 2 相对于 Ekstazi 和 STARTS 的平均精度违规率分别为 13.27% 和 9.01%。完整测试套件的平均变异分数为 18.90%；FLiRTS 2 的简化测试套件与突变分数平均偏差的标准偏差为 1.78%，Ekstazi 为 1.11%，STARTS 为 1.43%。我们的实验表明，FLiRTS 2 的性能接近基于代码的 RTS 的最先进工具，但需要的信息更少，执行选择的时间更短。