Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis

Understand

The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples.

• However, when the program semantics become more complex, it still remains a challenge to synthesize programs that are consistent with the specification.
• In this work, we propose SED, a neural program generation framework that incorporates synthesis, execution, and debugging stages.
• Instead of purely relying on the neural program synthesizer to generate the final program, SED first produces initial programs using the neural program synthesizer component, then utilizes a neural program debugger to iteratively repair the generated programs.