  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.
