规模很重要吗?或否:在汽车嵌入式软件中进行有限样本的A/B测试

Yuchu Liu, D. I. Mattos, J. Bosch, H. H. Olsson, Jonn Lantz
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引用次数: 6

摘要

A/B测试作为一种很有前途的工具,在汽车行业引起了人们的关注,它可以测量软件变更带来的偶然影响。与面向网络的业务(A/B测试已经建立起来)不同,汽车领域经常受到参与在线实验的合格用户有限的困扰。为了解决这一缺点,我们提出了一种设计平衡对照和治疗组的方法,以便从相当小的样本量的实验中得出合理的结论。虽然平衡匹配加权方法已经在医学等其他领域使用,但这是第一篇将其应用于软件开发环境并对其进行评估的论文。此外,我们详细描述了平衡匹配加权方法,并与一家汽车制造商一起进行了案例研究,将分组设计方法应用于车队。最后,我们介绍了我们在汽车软件工程领域的案例研究,并讨论了a /B组设计方法的优点和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Size matters? Or not: A/B testing with limited sample in automotive embedded software
A/B testing is gaining attention in the automotive sector as a promising tool to measure casual effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we present a method for designing balanced control and treatment groups so that sound conclusions can be drawn from experiments with considerably small sample sizes. While the Balance Match Weighted method has been used in other domains such as medicine, this is the first paper to apply and evaluate it in the context of software development. Furthermore, we describe the Balance Match Weighted method in detail and we conduct a case study together with an automotive manufacturer to apply the group design method in a fleet of vehicles. Finally, we present our case study in the automotive software engineering domain, as well as a discussion on the benefits and limitations of the A/B group design method.
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