Evaluating Software testability can assist software managers in optimizing testing budgets and identifying opportunities for refactoring. In this paper, we abandon the traditional approach of pursuing testability measurements based on the correlation between software metrics and test characteristics observed on past projects, e.g., the size, the organization or the code coverage of the test cases. We propose a radically new approach that exploits automatic test generation and mutation analysis to quantify the amount of evidence about the relative hardness of identifying effective test cases. We introduce two novel evidence-based testability metrics, describe a prototype to compute them, and discuss initial findings on whether our measurements can reflect actual testability issues.

中文翻译：

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迈向基于证据的可测性度量

评估软件可测试性可以帮助软件经理优化测试预算并确定重构机会。在本文中，我们放弃了基于软件指标与过去项目中观察到的测试特征（例如，测试用例的大小，组织或代码覆盖范围）之间的相关性来进行可测性测量的传统方法。我们提出了一种全新的方法，该方法利用自动测试生成和突变分析来量化有关识别有效测试用例的相对难度的证据数量。我们介绍了两个新颖的基于证据的可测试性指标，描述了计算它们的原型，并讨论了有关我们的测量是否可以反映实际可测试性问题的初步发现。