Growing solver-aided languages with rosette

Abstract

SAT and SMT solvers have automated a spectrum of programming tasks, including program synthesis, code checking, bug localization, program repair, and programming with oracles. In principle, we obtain all these benefits by translating the program (once) to a constraint system understood by the solver. In practice, however, compiling a language to logical formulas is a tricky process, complicated by having to map the solution back to the program level and extend the language with new solver-aided constructs, such as symbolic holes used in synthesis.