We consider the problem of generating automatic code given sample input-output pairs. We train a neural network to map from the current state and the outputs to the program's next statement. The neural network optimizes multiple tasks concurrently: the next operation out of a set of high level commands, the operands of the next statement, and which variables can be dropped from memory. Using our method we are able to create programs that are more than twice as long as existing state-of-the-art solutions, while improving the success rate for comparable lengths, and cutting the run-time by two orders of magnitude. Our code, including an implementation of various literature baselines, is publicly available at https://github.com/amitz25/PCCoder.
|Number of pages||10|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - 2018|
|Event||32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada|
Duration: 2 Dec 2018 → 8 Dec 2018