测试集直径：量化测试用例集的多样性
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
摘要：
在软件测试人员中，一个常见的和自然的直觉是，如果要正确地测试软件系统并确保其质量，则测试用例需要不同。因此，许多研究已经进入制定距离措施如何测试用例，他们的输入和/或他们的输出不同。然而，这些建议的共同之处在于它们是数据类型特定的和/或仅计算测试输入、迹线或输出对之间的差异。我们提出了一个新的度量标准来衡量测试集的多样性：测试集直径（TSDm）。它扩展了我们以前的成对测试多样性度量的基础上，在信息论的归一化压缩距离（NCD）的计算多集的最新进展。一个优点是TSDm可以应用于任何数据类型和任何测试相关信息，而不仅仅是测试输入。缺点是与竞争方法相比增加了计算时间。我们在四个不同的系统上的实验表明，测试集直径可以帮助选择测试集更高的结构和故障覆盖率比随机选择，即使只适用于测试输入。这可以使早期的测试设计和选择，甚至在有一个软件系统进行测试之前，并补充其他类型的测试自动化和分析。我们认为，这种量化的测试集多样性创造了一些机会，以更好地了解软件质量，并提供了切实可行的方法来提高它。
英文摘要：
A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. An advantage is that TSDm can be applied regardless of data type and on any test-related information, not only the test inputs. A downside is the increased computational time compared to competing approaches. Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.
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