Towards Security Defect Prediction with AI
Carnegie Mellon University Software Engineering Institute Pittsburgh United States
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In this study, we investigate the limits of the current state of the art AI system for detecting buffer overflows and compare it with current static analysis tools. To do so, we developed acode generator, s-bAbI, capable of producing an arbitrarily large number of code samples of controlled complexity. We found that the static analysis engines we examined have good precision, but poor recall on this dataset, except for a sound static analyzer that has good precision and recall. We found that the state of the art AI system, a memory network modeled after Choi et al. 1, can achieve similar performance to the static analysis engines, but requires an exhaustive amount of training data in order to do so. Our work points towards future approaches that may solve these problems namely, using representations of code that can capture appropriate scope information and using deep learning methods that are able to perform arithmetic operations.