A/B testing is a form of markov-chain-monte-carlo experimentation that makes certain assumptions about the landscape it is trying to find the maximum likelihood in. Specifically, it makes the assumption of monotonicity of the likelihood or that the lack of monotonicity is of the same scale as the changes they are likely to make. A/B testing cannot break out of a local maximum of the likelihood function if the assumptions are incorrect.
A complete rewrite of the application from media heavy to plain is far smaller than the typical stepsize used in A/B testing and has some temporal effects on top of it.