Improving Neural Program Synthesis with Inferred Execution Traces

The task of program synthesis, or automatically generating programs that are consistent with a provided specification, remains a challenging task in artificial intelligence. As in other fields of AI, deep learning-based end-to-end approaches have made great advances in program synthesis. However, compared to other fields such as computer vision, program synthesis provides greater opportunities to explicitly exploit structured information such as execution traces. While execution traces can provide highly detailed guidance for a program synthesis method, they are more difficult to obtain than more basic forms of specification such as input/output pairs. Therefore, we use the insight that we can split the process into two parts: infer traces from input/output examples, then infer programs from traces. Our application of this idea leads to state-of-the-art results in program synthesis in the Karel domain, improving accuracy to 81.3% from the 77.12% of prior work.