Specializing Meta Multi-Objectivization as Baseline for Software Engineering Optimization

원문 초록

Given the discrete nature of many software engineering (SE) optimization problems, trapping at local optima remains a crucial issue in the optimizer designs. Meta Multi-Objectivization (MMO) is an optimization model for overcoming local optima by introducing an extra, auxiliary objective. Yet, MMO is originally designed only for configuration tuning, leaving its suitability for other SE problems in doubt, particularly regarding the choice of the auxiliary objective. In this paper, we study the possibility and resolution for specializing MMO as a general baseline across a range of SE optimization problems, making it a fundamental counterpart for future SE optimizers that seek to mitigate the issue of local optima. We do so by providing both theoretical remit and empirical evidence: we analytically show how the choice of the auxiliary objective is crucial while demonstrating the effectiveness of MMO under nine SE optimization problems comprising 212 cases when the right one can be chosen, i.e., MMO is able to outperform the other state-of-the-art and general-purpose optimizers in 79% and 94% of the cases, respectively. Drawing on the theoretical understanding and empirical findings, we then propose L2RAC, a tool that automatically recommends the best auxiliary objective choice for a given SE problem/case, hence helping to specialize MMO therein. Through extensive experiments, we reveal that, in general, L2RAC can more accurately rank the choice of auxiliary objective for MMO under a case than other guesses by up to 60%, saving as much as 99% of the cost that would otherwise be required to conduct trial-and-error when using MMO.