Uncovering source code reuse in large‐scale academic environments

Abstract

The advent of the Internet has caused an increase in content reuse, including source code. The purpose of this research is to uncover potential cases of source code reuse in large-scale environments. A good example is academia, where massive courses are taught to students who must demonstrate that they have acquired the knowledge. The need of detecting content reuse in quasi real-time encourages the development of automatic systems such as the one described in this paper for source code reuse detection.Our approach is based on the comparison of programs at character level. It is able to find potential cases of reuse across a huge number of assignments. It achieved better results than JPlag, the most used online system to find similarities among multiple sets of source codes. The most common obfuscation operations we found were changes in identifier names, comments and indentation.

Author Profiling and Plagiarism Detection

“…With respect to plagiarism detection, recently it has been approached also in source code [12,13], and a PAN shared task on the detection of SOurce COde (SOCO) re-use has been organised at the Forum for Information Retrieval Evaluation.…”

“…Evaluation Corpora In the author profiling task at PAN 2013 [58] participants approached the task of identifying age and gender in a large corpus collected from social media, and age was annotated with three classes: 10s (13)(14)(15)(16)(17), 20s (23)(24)(25)(26)(27), and 30s (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47). At PAN 2014, we continued to study the gender and age aspects of the author profiling problem, however, four corpora of different genres were considered-social media, blogs, Twitter, and hotel reviews-both in English and Spanish.…”

“…Schler et al [61] studied the effect of age and gender in the writing style in blogs; they gathered over 71,000 blogs and obtained a set of stylistic features like non-dictionary words, parts-of-speech, function words and hyperlinks, combined with content features, such as word unigrams with the highest information gain. They modeled age in three classes -10s (13)(14)(15)(16)(17), 20s (23)(24)(25)(26)(27) and 30s (33-47)-obtaining an accuracy of about 80% for gender identification and about 75% for age identification. They showed that language features in blogs correlate with age, as reflected in, for example, the use of prepositions and determiners.…”

DOCODE 3.0 (DOcument COpy DEtector): A system for plagiarism detection by applying an information fusion process from multiple documental data sources

“…Existing literature on each topic is vast, so some authors have already surveyed approaches in automatic plagiarism detection. Here, we merely give reference to the most important studies, including [14,2,17,28]. …”

On the Detection of SOurce COde Re-use

“…Hence, the task of detecting source code re-use becomes even more difficult, since all the source codes will contain (to some extent) a considerable thematic overlap. On the other hand, detection of source code re-use in programming environments, such as programming contests, has an additional challenge, this is the large number of source codes that must be processed for detecting such practises [10], and as a result, source code reuse detection becomes some how unfeasible. Consequently, most of the research on source code re-use detection has been mostly applied to closed groups [23,20,13].…”