Size matters? Or not: A/B testing with limited sample in automotive embedded software
Abstract
A/B testing is gaining attention in the automotive sector as a promising tool to measure causal 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.
Cite
@article{arxiv.2107.02461,
title = {Size matters? Or not: A/B testing with limited sample in automotive embedded software},
author = {Yuchu Liu and David Issa Mattos and Jan Bosch and Helena Holmström Olsson and Jonn Lantz},
journal= {arXiv preprint arXiv:2107.02461},
year = {2021}
}
Comments
In proceedings of the 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)