DeepCoder: Learning to Write Programs

Understand

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

• The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs.
• We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver.
• Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.