  Intelligent code analysis has received increasing attention in parallel with
the 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 code
in a useful form that ML algorithms can accept as input. In this study, we
present a novel method to represent source code as image while preserving
semantic and syntactic properties, which paves the way for leveraging computer
vision techniques to use for code analysis. Indeed the method makes it possible
to directly enter the resulting image representation of source codes into deep
learning (DL) algorithms as input without requiring any further data
pre-processing or feature extraction step. We demonstrate feasibility and
effectiveness of our method by realizing a vulnerability prediction use case
over a public dataset containing a large number of real-world source code
samples with performance evaluation in comparison to the state-of-art
solutions. Our implementation is publicly available.
