提出可组合的测试用例优先级方法，显著提升回归测试效率
Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators
- 构建可组合的测试用例优先级方法框架，融合多种策略
- 在RTPTorrent数据集上表现优于基线方法，提升效率达2.7%
- 适合关注测试优化与自动化研究的开发者和工程师
背景：测试用例优先级（TCP）是软件开发组织中广泛使用的加速回归测试的技术。目标：系统化现有TCP知识，并提出并实证评估一种新方法。方法：对TCP开展雪崩式文献综述（SR），实现一个全面的TCP研究平台（TCPFramework），分析现有评估指标并提出两个新指标（rAPFDc{} 和 ATR），开发一类称为‘方法组合器’的集成型TCP方法。结果：SR识别出324篇相关研究。所提技术在RTPTorrent数据集上评估，多数被测程序中均优于基线方法，其性能在rAPFDc{}、NTR和ATR指标上与当前最优启发式算法相当，且采用不同思路。结论：该方法可用于高效TCP，使回归测试时间最多减少2.7%。方法组合器因具备组合性，为未来TCP研究提供了显著改进潜力。
原文摘要 · Abstract (English)
Context: Test case prioritization (TCP) is a technique widely used by software development organizations to accelerate regression testing. Objectives: We aim to systematize existing TCP knowledge and to propose and empirically evaluate a new TCP approach. Methods: We conduct a snowballing review (SR) on TCP, implement a~comprehensive platform for TCP research (TCPFramework), analyze existing evaluation metrics and propose two new ones (\rAPFDc{} and ATR), and develop a~family of ensemble TCP methods called approach combinators. Results: The SR helped identify 324 studies related to TCP. The techniques proposed in our study were evaluated on the RTPTorrent dataset, consistently outperforming their base approaches across the majority of subject programs, and achieving performance comparable to the current state of the art for heuristical algorithms (in terms of \rAPFDc{}, NTR, and ATR), while using a distinct approach. Conclusions: The proposed methods can be used efficiently for TCP, reducing the time spent on regression testing by up to 2.7\%. Approach combinators offer significant potential for improvements in future TCP research, due to their composability.
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