Towards Automated A/B Testing

User-intensive software, such as Web and mobile applications, heavily depends on the interactions with large and unknown populations of users. Knowing the preferences and behaviors of these populations is crucial for the success of this class of systems. A/B testing is an increasingly popular technique that supports the iterative development of user-intensive software based on controlled experiments performed on live users. However, as currently performed, A/B testing is a time consuming, error prone and costly manual activity. In this paper, we investigate a novel approach to automate A/B testing. More specifically, we rephrase A/B testing as a search-based software engineering problem and we propose an initial approach that supports automated A/B testing through aspect-oriented programming and genetic algorithms.