Neural Attribute Grammars for Semantics-Guided Program Generation Swarat Chaudhuri UT Austin

Abstract:

I will talk about Neural Attribute Grammars (NAG), a framework for deep statistical generation of source code modulo language-level semantic requirements (such as type safety or initialization of variables before use). Neural models for source code have received significant attention in the recent past. However, these models tend to be trained on syntactic program representations, and consequently, often generate programs that violate essential semantic invariants. In contrast, the NAG framework exposes the semantics of the target language to the training procedure for the neural model using attribute grammars. During training, the model learns to replicate the relationship between the syntactic rules used to construct a program, and the semantic attributes (for example, symbol tables) of the context in which the rule is fired. In the talk, I will give some concrete examples of NAGs and show how to use them in the conditional generation of Java programs. I will demonstrate that these NAGs generate semantically "sensible" programs with significantly higher frequency than traditional neural models of source code.

(This talk is based on joint work with Rohan Mukherjee, Chris Jermaine, Tom Reps, Dipak Chaudhari, and Matt Amodio.)

Bio: Swarat Chaudhuri is an Associate Professor of computer science at the University of Texas at Austin. His research studies topics in the intersection of machine learning and programming languages, including program induction, probabilistic programming, neurosymbolic programming, programmatically interpretable/explainable learning, learning-accelerated formal reasoning, and formally certified learning. Swarat received a bachelor's degree from the Indian Institute of Technology, Kharagpur, in 2001, and a doctoral degree from the University of Pennsylvania in 2007. Before joining UT Austin, he held faculty positions at Rice University and the Pennsylvania State University. He is a recipient of the National Science Foundation CAREER award, the ACM SIGPLAN John Reynolds Doctoral Dissertation Award, and the Morris and Dorothy Rubinoff Dissertation Award from the University of Pennsylvania.