Automating Pipelines of A/B Tests with Population Split Using Self-Adaptation and Machine Learning

A/B testing is a common approach used in industry to facilitate innovation through the introduction of new features or the modification of existing software. Traditionally, A/B tests are conducted sequentially, with each experiment targeting the entire population of the corresponding application. This approach can be time-consuming and costly, particularly when the experiments are not relevant to the entire population. To tackle these problems, we introduce a new self-adaptive approach called AutoPABS, short for Automated Pipelines of A/B tests using Self-adaptation, that (1) automates the execution of pipelines of A/B tests, and (2) supports a split of the population in the pipeline to divide the population into multiple A/B tests according to user-based criteria, leveraging machine learning. We started the evaluation with a small survey to probe the appraisal of the notation and infrastructure of AutoPABS. Then we performed a series of tests to measure the gains obtained by applying a population split in an automated A/B testing pipeline, using an extension of the SEAByTE artifact. The survey results show that the participants express the usefulness of automating A/B testing pipelines and population split. The tests show that automatically executing pipelines of A/B tests with a population split accelerates the identification of statistically significant results of the parallel executed experiments of A/B tests compared to a traditional approach that performs the experiments sequentially.