
Academic dishonesty is a universal problem. The educational community across the world is facing the increasing problem of plagiarism. This widespread problem has motivated the need of an efficient, robust and fast detection procedure that is difficult to be achieved manually. Detecting duplicated text among natural language artifacts is a well-documented task. However, performing similar analysis on source code presents unique problems. Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. In this paper, a brief discussion of source code Plagiarism is presented as well as comparative study of the application of various techniques in textual similarity processing on source code.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
