Size matters? Or not: A/B testing with limited sample in automotive embedded software
This addresses the problem of limited eligible users for online experiments in the automotive sector, though it is incremental as it applies an existing method to a new domain.
The paper tackles the challenge of conducting A/B testing with limited sample sizes in automotive embedded software by presenting a method for designing balanced control and treatment groups, enabling sound conclusions from small experiments, as demonstrated in a case study with an automotive manufacturer.
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.