从数据集中提取时间模型

Nour Chetouane, F. Wotawa
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引用次数: 1

摘要

测试的目标是找到与被测系统之间导致意外行为的交互。这样的交互是测试用例,可以手工指定,也可以自动生成。对于后者,我们在研究文献中找到了许多方法和技术,包括组合测试或基于模型的测试。在本文中,我们关注基于模型的自动化测试用例生成,我们感兴趣的是从可用数据中提取模型。我们特别考虑了汽车测试,其中汽车和其他车辆必须在典型的驾驶情况下正确运行。我们的想法是使用可用的驾驶数据,从中提取驾驶模型,我们可以稍后用于生成测试用例,即车辆测试的任意驾驶模式。除了概述基础之外,我们还讨论了我们使用可用的开放获取驾驶数据获得的第一个实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extracting Temporal Models from Data Episodes
The testing objective is to find interactions with a system under test leading to unexpected behavior. Such interactions are test cases that can be either manually specified or automatically generated. For the latter, we find many methods and techniques in the research literature, including combinatorial testing or model-based testing. In this paper, we focus on automated test case generation based on models where we are interested in extracting models from available data. In particular, we consider automotive testing, where cars and other vehicles must behave correctly in typical driving situations. The idea is to use available driving data from which we want to extract driving models that we can later use for generating test cases, i.e., arbitrary driving patterns for vehicle testing. Besides outlining the foundations, we discuss the first experimental results we obtain using available open-access driving data.
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