Second, we present a self-supervised learning paradigm for program repair that leverages unlabeled programs available online to create a large amount of extra program repair examples, which we use to pre-train our models.
We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search.
Program synthesis is the task of automatically generating a program consistent with a given specification.
The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models.
Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial.
Implementing enterprise process automation often requires significant technical expertise and engineering effort.
Program synthesis has recently emerged as a promising approach to the image parsing task.
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool.
Puzzles are objective in that one can easily test the correctness of a given solution x by seeing whether it satisfies f, unlike the most common representations for program synthesis: given input-output pairs or an English problem description, the correctness of a given solution is not determined and is debatable.