  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. The integration
of the debugger component enables SED to modify the programs based on the
execution results and specification, which resembles the coding process of
human programmers. On Karel, a challenging input-output program synthesis
benchmark, SED reduces the error rate of the neural program synthesizer itself
by a considerable margin, and outperforms the standard beam search for
decoding.
