Deep Learning to Find Bugs
Michael Pradel, Koushik Sen. 2017
Automated bug detection, e.g., through pattern-based static analysis, is an increasingly popular technique to find programming errors and other code quality issues. Traditionally, bug detectors are program analyses that are manually written and carefully tuned by an analysis expert. Unfortunately, the huge amount of possible bug patterns makes it difficult to cover more than a small fraction of all bugs. This paper presents a new approach toward creating bug detectors. The basic idea is to replace manually writing a program analysis with training a machine learning model that distinguishes buggy from non-buggy code. To address the challenge that effective learning requires both positive and negative train- ing examples, we use simple code transformations that create likely incorrect code from existing code examples. We present a general framework, called DeepBugs, that extracts positive training examples from a code corpus, leverages simple program transformations to create negative training examples, trains a model to distinguish these two, and then uses the trained model for identifying programming mistakes in previously unseen code. As a proof of concept, we create four bug detectors for JavaScript that find a diverse set of programming mistakes, e.g., accidentally swapped function arguments, incorrect assignments, and incorrect binary operations. To find bugs, the trained models use information that is usually discarded by program analyses, such as identifier names of variables and functions. Applying the approach to a corpus of 150,000 JavaScript files shows that learned bug detectors have a high accuracy, are very efficient, and reveal 132 programming mistakes in real-world code.
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