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Online A/B testing has been widely used by software companies to evaluate theimpact of new technologies by offering it to a groups of users and comparingagainst the unmodified product. However, running online A/B testing needs notonly efforts in design, implementation and stakeholders' approval to be servedin production, but also several weeks to collect the data in iterations. Toaddress these issues, a recent emerging topic, called \textit{offline A/Btesting}, is getting increasing attention, with the goal to conduct offlineevaluation of a new technology by estimating historical logged data. Althoughthis approach is promising due to lower implementation effort, fasterturnaround time and no potential user harm, for it to be effectivelyprioritized as requirements in practice, several limitations need to beaddressed, including its discrepancy with online A/B test results, and lack ofsystematic updates on new data. In response, in this vision paper, we introduceAutoOffAB, an idea to automatically runs variants of offline A/B testingagainst recent logging and update the offline evaluation results, which areused to make decisions on requirements more reliably and systematically.

Can Offline A/B Testing Be Automated for Data-Driven Requirement Engineering?