Learning a Static Analyzer: A Case Study on a Toy Language

Manzil Zaheer, Jean-Baptiste Tristan, Michael L. Wick, Guy L. Steele Jr.

Abstract:

Static analyzers are meta-programs that analyze programs to detect
potential errors or collect information. For example, they are used
as security tools to detect potential buffer overflows. Also, they
are used by compilers to verify that a program is well-formed and
collect information to generate better code. In this paper, we
address the following question: can a static analyzer be learned
from data? More specifically, can we use deep learning to learn a
static analyzer without the need for complicated feature
engineering? We show that long short-term memory networks are able
to learn a basic static analyzer for a simple toy language. However,
pre-existing approaches based on feature engineering, hidden Markov
models, or basic recurrent neural networks fail on such a simple
problem. Finally, we show how to make such a tool usable by
employing a language model to help the programmer detect where the
reported errors are located.

Conflicts: harvard.edu, cmu.edu, oracle.com, umass.edu