基于分段的测试用例优先级排序：一种多目标方法
Segment-Based Test Case Prioritization: A Multi-objective Approach
AI总结：
本文针对UI测试中源代码信息缺失的测试用例优先级排序问题，提出一种基于网页分割的多目标进化优化方法，在自建数据集上取得最高APFD和APFDc分数，显著优于现有方法。
摘要：
软件回归测试是一项至关重要但耗时的工作，尤其是在用户界面（UI）测试场景中，需要同时验证多个微服务。测试用例优先级排序（TCP）是一种经济高效的解决方案，它通过按照能够最大化目标函数的执行顺序来调度测试用例，通常旨在提高故障检测率。虽然已经提出了多种TCP技术，但大多数都依赖源代码信息，而这些信息在UI测试中通常无法获得。本文提出了一种多目标优化方法，用于对UI测试用例进行优先级排序，使用进化搜索算法和四个以网页元素为中心、作为优化问题目标的覆盖率标准。我们的方法不需要源代码信息，使用两种进化算法（AGE-MOEA和NSGA-II）进行评估，并在一个自行收集的包含11个测试套件的数据集上与其他TCP方法进行比较。结果表明，我们的方法在平均故障检测百分比（APFD）和带成本的APFD（APFDc）方面显著优于其他方法，分别取得了87.8%和79.2%的最高分。我们还引入了一个新的数据集，并通过实证实验证明了我们的方法相对于现有方法的显著改进。本文的贡献包括：将网页分割应用于TCP、为UI TCP构建了一个新数据集，以及通过实证比较证明了我们方法的改进。
英文摘要：
Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient solution to address this by scheduling test cases in an execution order that maximizes an objective function, generally aimed at increasing the fault detection rate. While several techniques have been proposed for TCP, most rely on source code information which is usually not available for UI testing. In this paper, we introduce a multi-objective optimization approach to prioritize UI test cases, using evolutionary search algorithms and four coverage criteria focusing on web page elements as objectives for the optimization problem. Our method, which does not require source code information, is evaluated using two evolutionary algorithms (AGE-MOEA and NSGA-II) and compared with other TCP methods on a self-collected dataset of 11 test suites. The results show that our approach significantly outperforms other methods in terms of Average Percentage of Faults Detected (APFD) and APFD with Cost (APFDc), achieving the highest scores of 87.8\% and 79.2\%, respectively. We also introduce a new dataset and demonstrate the significant improvement of our approach over existing ones via empirical experiments. The paper's contributions include the application of web page segmentation in TCP, the construction of a new dataset for UI TCP, and empirical comparisons that demonstrate the improvement of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
