一句话：提出 FAST 系列技术，借助大数据中的相似项检索算法，实现可扩展的白盒与黑盒相似度测试用例优先级排序。
基于相似度的测试用例优先级排序准则倾向于优先选择与已选用例差异最大的测试用例，从而加速故障检测，但已有准则难以应对现代工业系统中数千乃至数百万规模的测试套件，实践中只能退而使用简单启发式。本文提出 FAST 系列测试用例优先级排序技术，其核心思路是借用大数据领域常用于查找相似项的算法来重构相似度计算与选择过程，从而同时支持白盒与黑盒两种形式的可扩展相似度排序。作者在真实 C 与 Java 对象上开展实验：该家族中最快的成员在效率上优于其他黑盒方法且有效性无显著损失；在不计准备时间时，也优于包括贪心方法在内的白盒方法。可扩展性模拟研究进一步表明，其中一种 FAST 技术可在 20 分钟内完成一百万个测试用例的优先级排序。
Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.
@inproceedings{paperbot4410,
title = {FAST approaches to scalable similarity-based test case prioritization},
author = {Breno Miranda and Emilio Cruciani and Roberto Verdecchia and Antonia Bertolino},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180210}
}