Oracle-guided inductive synthesis (OGIS) is a widely-used framework to apply program synthesis techniques in practice. The question selection problem aims at reducing the number of iterations in OGIS by selecting a proper input for each OGIS iteration. Theoretically, a question selector can generally improve the performance of OGIS solvers on both interactive and non-interactive tasks if it is not only effective for reducing iterations but also efficient. However, all existing effective question selectors fail in satisfying the requirement of efficiency. To ensure effectiveness, they convert the question selection problem into an optimization one, which is difficult to solve within a short time.

In this paper, we propose a novel question selector, named \textit{LearnSy}. \textit{LearnSy} is both efficient and effective and thus achieves general improvement for OGIS solvers for the first time. Since we notice that the optimization tasks in previous studies are difficult because of the complex behavior of operators, we estimate these behaviors in \textit{LearnSy} as simple random events. Subsequently, we provide theoretical results for the precision of this estimation and design an efficient algorithm for its calculation.

According to our evaluation, when dealing with interactive tasks, \textit{LearnSy} can offer competitive performance compared to existing selectors while being more efficient and more general. Moreover, when working on non-interactive tasks, \textit{LearnSy} can generally reduce the time cost of existing CEGIS solvers by up to $43.0%$.