Predicting bugs using symbolic execution graph

Many bug prediction algorithms have been developed by academia, and also some algorithms were adopted by the industry. These algorithms help reviewers to better distribute their time among patches, since some of them require more care. Symbolic execution is a static analysis method that can be used to find bugs. In this paper, we introduce a new bug prediction algorithm that is based on symbolic execution. Instead of using the results of this static analysis method we use the shape of the symbolic execution graph and how it can be mapped back to the original source code to predict bugs. We also investigate how this metric correlates with well-known methods such as the Rahman metric or Time Weighted Risk.