- 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