Chris Jermaine

Finding Likely Errors with Bayesian Specifications

2017 · arXiv (Cornell University)

We present a Bayesian framework for learning probabilistic specifications from large, unstructured code corpora, and a method to use this framework to statically detect anomalous, hence likely buggy, program behavior. The distinctive insight here is …

Neural Attribute Grammars for Semantics-Guided Program Generation

2017 · arXiv (Cornell University)

Existing deep models for code tend to be trained on syntactic program representations. We present an alternative, called Neural Attribute Grammars, that exposes the semantics of the target language to the training procedure using an …