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Adaptive Metamorphic Testing with Contextual Bandits
arXiv - CS - Software Engineering Pub Date : 2019-10-01 , DOI: arxiv-1910.00262
Helge Spieker, Arnaud Gotlieb

Metamorphic Testing is a software testing paradigm which aims at using necessary properties of a system-under-test, called metamorphic relations, to either check its expected outputs, or to generate new test cases. Metamorphic Testing has been successful to test programs for which a full oracle is not available or to test programs for which there are uncertainties on expected outputs such as learning systems. In this article, we propose Adaptive Metamorphic Testing as a generalization of a simple yet powerful reinforcement learning technique, namely contextual bandits, to select one of the multiple metamorphic relations available for a program. By using contextual bandits, Adaptive Metamorphic Testing learns which metamorphic relations are likely to transform a source test case, such that it has higher chance to discover faults. We present experimental results over two major case studies in machine learning, namely image classification and object detection, and identify weaknesses and robustness boundaries. Adaptive Metamorphic Testing efficiently identifies weaknesses of the tested systems in context of the source test case.

中文翻译:

使用上下文强盗的自适应变形测试

变形测试是一种软件测试范式,旨在使用被测系统的必要属性(称为变形关系)来检查其预期输出或生成新的测试用例。变形测试已经成功地测试了没有完整预言机的程序,或者测试了预期输出具有不确定性的程序,例如学习系统。在本文中,我们建议将自适应变形测试作为一种简单而强大的强化学习技术(即上下文强盗)的概括,以选择可用于程序的多种变形关系之一。通过使用上下文老虎机,自适应变形测试了解哪些变形关系可能会转换源测试用例,从而有更高的机会发现故障。我们展示了机器学习中两个主要案例研究的实验结果,即图像分类和对象检测,并确定弱点和鲁棒性边界。自适应变形测试在源测试用例的上下文中有效地识别被测试系统的弱点。
更新日期:2020-06-23
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