输入度量关系分析
An analysis of the relationship of input metrics
- CISPA Helmholtz Center for Information Security（CISPA赫尔姆霍兹信息安全中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文利用分区测试方法系统比较常见输入度量，提出新度量k-alt-path以减少冗余并提高敏感性，为未来度量改进研究奠定基础。
AI中文摘要：
输入度量根据测试套件中存在的输入特征来评估测试的进展。早在20世纪50年代，先前的工作就建立了许多这样的度量，但很少有工作致力于对它们进行比较。本文通过利用分区测试文献中为其他度量类别提出的现有方法来进行比较。在定义和回顾常见输入度量之后，我们首先进行了一个简短的案例研究，揭示典型的经验比较策略在本质上不足以比较度量。然后，我们通过定义和实现$k$-alt-path（一种新度量，在提高对$k$-path敏感性的同时减少冗余）来展示如何严格改进标准度量。随后，在讨论我们发现的启示之前，我们对其他每种常见输入度量进行了系统比较。通过这些贡献，我们提出了分区测试分析方法，这些方法为未来改进输入度量的研究提供了依据并形成了策略。
英文摘要：
Input metrics evaluate the progress of testing in terms of features of inputs present in a test suite. Previous works, as early as the 1950s, established a number of such metrics, but few endeavored to compare them. This paper does so by utilizing existing methods proposed for other metric classes in partition testing literature. After defining and reviewing common input metrics, we begin with a short case study revealing that typical empirical comparison strategies are fundamentally insufficient for comparing metrics. Then, we demonstrate how one rigorously improves a standard metric by defining and implementing $k$-alt-path, a new metric which reduces redundancy while improving sensitivity over $k$-path. Each of the other common input metrics are then systematically compared before discussing the implications of our findings. With these contributions, we bring forward partition testing analysis methods that justify and form a strategy for future research in refining input metrics.
