Bottom-up synthesis of recursive functional programs using angelic execution

We present a novel bottom-up method for the synthesis of functional recursive programs. While techniques can work better than top-down methods in certain settings, there is no prior technique synthesizing programs from logical specifications purely fashion. The main challenge that effective need to execute sub-expressions code being synthesized, but it impossible subexpression program has not been fully constructed yet. In this paper, we address using concept angelic semantics. Specifically, our finds satisfies specification under semantics (we refer as synthesis), analyzes assumptions made during its execution, uses analysis strengthen specification, and finally reattempts with strengthened specification. Our proposed algorithm based on version space learning therefore deals effectively many incremental calls overall algorithm. have implemented approach prototype called Burst evaluate problems work. experiments show able synthesize solution 94% benchmarks benchmark suite, outperforming