t-wise Coverage by Uniform Sampling
t-wise Coverage by Uniform Sampling
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DOI：
10.1145/3336294.3342359
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Jeho Oh;Paul Gazzillo;D. Batory
中科院分区：
文献类型：
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作者：
Jeho Oh;Paul Gazzillo;D. Batory
有效地测试软件产品线（SPL）的大配置空间需要一个采样算法，既可扩展，并提供良好的t-wise覆盖。2019年SPLC采样挑战赛提供了大型的真实世界特征模型，并要求提供可用于这些模型的t-wise采样算法。我们通过对所提供的特征模型之一的配置进行均匀采样（美国）来评估t级覆盖率。US意味着每个（法律的）配置被选择的可能性相等。US产生配置空间的统计上有代表性的样本，并且可以用作比较其他采样算法的基线。我们使用现有的算法称为Smarch均匀采样SPL配置。虽然单独的均匀采样不足以产生100%的1-wise和2-wise覆盖率，我们使用标准的概率分析来解释我们的实验结果，并推测均匀采样如何提高现有的t-wise采样算法的可扩展性。
Efficiently testing large configuration spaces of Software Product Lines (SPLs) needs a sampling algorithm that is both scalable and provides good t-wise coverage. The 2019 SPLC Sampling Challenge provides large real-world feature models and asks for a t-wise sampling algorithm that can work for those models. We evaluated t-wise coverage by uniform sampling (US) the configurations of one of the provided feature models. US means that every (legal) configuration is equally likely to be selected. US yields statistically representative samples of a configuration space and can be used as a baseline to compare other sampling algorithms. We used existing algorithm called Smarch to uniformly sample SPL configurations. While uniform sampling alone was not enough to produce 100% 1-wise and 2-wise coverage, we used standard probabilistic analysis to explain our experimental results and to conjecture how uniform sampling may enhance the scalability of existing t-wise sampling algorithms.