Guiding dynamic programing via structural probability for accelerating programming by example

Programming by example (PBE) is an important subproblem of program synthesis, and PBE techniques have been applied to many domains. Though many techniques for accelerating PBE systems have been explored, the scalability remains one of the main challenges: There is still a gap between the performances of state-of-the-art synthesizers and the industrial requirement. To further speed up solving PBE tasks, in this paper, we propose a novel PBE framework MaxFlash. MaxFlash uses a model based on structural probability, named topdown prediction models, to guide a search based on dynamic programming, such that the search will focus on subproblems that form probable programs, and avoid improbable programs. Our evaluation shows that MaxFlash achieves × 4.107− × 2080 speed-ups against state-of-the-art solvers on 244 real-world tasks.