Poster: Is Euclidean Distance the best Distance Measurement for Adaptive Random Testing?

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI:10.1109/icst46399.2020.00049

Rubing Huang, Chenhui Cui, Weifeng Sun, D. Towey

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引用次数: 9

Abstract

Adaptive random testing (ART) aims at enhancing the testing effectiveness of random testing (RT) by more evenly spreading test cases over the input domain. Many ART methods have been proposed, based on various, different notions. For example, distance-based ART (DART) makes use of the concept of distance to implement ART, attempting to generate new test cases that are far away from previously executed ones. The Euclidean distance has been a popular choice of distance metric, used in DART to evaluate the differences between test cases. However, is the Euclidean distance the most suitable choice for DART? To answer this question, we conducted a series of simulations to investigate the impact that the Euclidean distance, and its many variations, has on the testing effectiveness of DART. The results show that when the dimensionality of the input domain is low, the Euclidean distance may indeed be a good choice. However, when the dimensionality is high, it appears to be less suitable.

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海报:欧几里得距离是自适应随机测试的最佳距离度量吗?

自适应随机测试(ART)旨在通过在输入域上更均匀地分布测试用例来提高随机测试(RT)的测试效率。基于各种不同的概念，提出了许多抗逆转录病毒治疗方法。例如，基于距离的ART (DART)利用距离的概念来实现ART，试图生成远离先前执行的测试用例的新测试用例。欧几里得距离一直是距离度量的一种流行选择，在DART中用于评估测试用例之间的差异。然而，欧几里得距离是DART最合适的选择吗?为了回答这个问题，我们进行了一系列模拟，以研究欧几里得距离及其许多变化对DART测试有效性的影响。结果表明，当输入域的维数较低时，欧几里得距离确实可能是一个很好的选择。然而，当维度较高时，它似乎不太适合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)

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