A Source Code Similarity System for Plagiarism Detection

Abstract

Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Those systems need to recognize a number of lexical and structural source code modifications. For example, by some structural modifications (e.g., modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. Most of the existing source code similarity detection systems can be confused when these structural modifications have been applied to the original source code. In order to be considered effective, a source code similarity detection system must address these issues. In order to address them, we designed and developed the source code similarity system for plagiarism detection. To demonstrate that the proposed system has the desired effectiveness, we performed a wellknown conformism test. The proposed system showed promising results as compared to the JPlag system in detecting source code similarity when various lexical or structural modifications are applied to plagiarized code. As a confirmation of these results, an independent samples t-test revealed that there was a statistically significant difference between average values of F-measures for the test sets that we used and for the experiments that we have done in the practically usable range of cutoff threshold values of 35%-70%.

A comparison of code similarity analysers

“…Different similarity measurements such as suffix trees, string alignment, Jaccard similarity, etc., can be applied to sequences or sets of tokens. Tools that rely on tokens include Sherlock (Joy and Luck 1999), BOSS (Joy et al 2005), Sim (Gitchell and Tran 1999), YAP3 (Wise 1996), JPlag (Prechelt et al 2002), CCFinder (Kamiya et al 2002), CP-Miner (Li et al 2006), MOSS (Schleimer et al 2003), Burrows et al (2007), and the Source Code Similarity Detector System (SCSDS) (Duric and Gasevic 2013). The token-based representation is widely used in source code similarity measurement and very efficient on a scale of millions SLOC.…”

“…Using source and bytecode obfuscators, we can create pervasively modified source code that contains modifications of lexical and structural changes. We have investigated the code transformations offered by Artifice and ProGuard and found that they cover changes commonly found in both code cloning and code plagiarism as reported by , Schulze and Meyer (2013), Duric and Gasevic (2013), Joy and Luck (1999), and Brixtel et al (2010). The details of code modifications supported by our obfuscators are shown in Table 1.…”

“…Formatting changes Duric and Gasevic 2013;Joy and Luck 1999) Addition, modification or deletion of comments (Duric and Gasevic 2013;Joy and Luck 1999) Renaming of identifiers, methods Duric and Gasevic 2013;Joy and Luck 1999;Brixtel et al 2010;Fowler 2013) Modification of constant values (Duric and Gasevic 2013) …”

“…Split or merge of variable declarations (Duric and Gasevic 2013) Addition, modification or deletion of modifiers (Duric and Gasevic 2013;Fowler 2013) Line insertion/deletion with further edits Reordering of statements & control replacements Duric and Gasevic 2013;Joy and Luck 1999;Brixtel et al 2010) Modification of control structures (Duric and Gasevic 2013;Joy and Luck 1999;Brixtel et al 2010) Changing of data types and modification of data structures (Duric and Gasevic 2013) Method inlining and method refactoring (Duric and Gasevic 2013;Fowler 2013) Structural redesign of source code (Duric and Gasevic 2013;Fowler 2013) Since each decompiler has its own decompiling algorithm, one decompiler usually generates source code which is different from the source code generated by other decompilers. Using more than one decompiler can also be a method of obfuscation by creating variants of the same program with the same semantics but with different source code.…”

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

“…number of operator and operand); and structure-based approach determines similarity based on source code structure. Among these approaches, structure-based approach is the most popular one due to its effectiveness [6]. This approach is quite sensitive to instruction order which is important on determining source code plagiarism.…”