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Intelligent code analysis has received increasing attention in parallel withthe remarkable advances in the field of machine learning (ML) in recent years.A major challenge in leveraging ML for this purpose is to represent source codein a useful form that ML algorithms can accept as input. In this study, wepresent a novel method to represent source code as image while preservingsemantic and syntactic properties, which paves the way for leveraging computervision techniques to use for code analysis. Indeed the method makes it possibleto directly enter the resulting image representation of source codes into deeplearning (DL) algorithms as input without requiring any further datapre-processing or feature extraction step. We demonstrate feasibility andeffectiveness of our method by realizing a vulnerability prediction use caseover a public dataset containing a large number of real-world source codesamples with performance evaluation in comparison to the state-of-artsolutions. Our implementation is publicly available.

Code2Image: Intelligent Code Analysis by Computer Vision Techniques and Application to Vulnerability Prediction