Predictive constraint solving and analysis

Abstract

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We introduce a new idea for enhancing constraint solving engines that drive many analysis and synthesis techniques that are powerful but have high complexity. Our insight is that in many cases the engines are run repeatedly against input constraints that encode problems that are related but of increasing complexity, and domain-specific knowledge can reduce the complexity. Moreover, even for one formula the engine may perform multiple expensive tasks with commonalities that can be estimated and exploited. We believe these relationships lay a foundation for making the engines more effective and scalable. We illustrate the viability of our idea in the context of a well-known solver for imperative constraints, and discuss how the idea generalizes to more general purpose methods.