Neural Program Generation Modulo Static Analysis

Understand

State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as the generation of entire method bodies.

• We propose to address this deficiency using weak supervision from a static program analyzer.
• Our neurosymbolic method allows a deep generative model to symbolically compute, using calls to a static-analysis tool, long-distance semantic relationships in the code that it has already generated.
• During training, the model observes these relationships and learns to generate programs conditioned on them.