Current Trends in Source Code Analysis, Plagiarism Detection and Issues of Analysis Big Datasets

In this work, we analyze the state of the art in source code analysis area with a focus on plagiarism detection and provide a proposal for a future work in this area. Detection of plagiarism combines the detection of clones and methods for determining similarity. Nowadays, there are several approaches that can be divided into three levels. The first one is text based and uses plain text as an input. The second level is token based. The top level is model based and uses models to represent source code. These advanced algorithms (token and model based) can't work with large datasets. We believe the future belongs to the algorithms that will be able to handle large amount of source code. These algorithms should use one of model-based representations. They can be used for formation of large-scale anti-plagiarism systems. They can be used also in the area of source code optimization.