Source Code Representations for Plagiarism Detection

2018 ◽  
pp. 61-69 ◽  
Author(s):  
Michal Ďuračík ◽  
Emil Kršák ◽  
Patrik Hrkút
Keyword(s):  
Source Code ◽  
Plagiarism Detection

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Source Code Plagiarism Detection in an Educational Context: A Literature Mapping

2021 ◽  
Author(s):  
Rodrigo C Aniceto ◽  
Maristela Holanda ◽  
Carla Castanho ◽  
Dilma Da Silva
Keyword(s):  
Source Code ◽  
Educational Context ◽  
Plagiarism Detection ◽  
Literature Mapping

Plagiarism Detection Algorithm for Source Code in Computer Science Education

2015 ◽  
Vol 13 (4) ◽  
pp. 29-39 ◽  
Author(s):  
Xin Liu ◽  
Chan Xu ◽  
Boyu Ouyang
Keyword(s):  
Hash Function ◽  
Source Code ◽  
College Education ◽  
Detection Algorithm ◽  
Longest Common Subsequence ◽  
The Other ◽  
Plagiarism Detection ◽  
Detection Algorithms ◽  
Common Subsequence ◽  
Basic Concepts

Nowadays, computer programming is getting more necessary in the course of program design in college education. However, the trick of plagiarizing plus a little modification exists among some students' home works. It's not easy for teachers to judge if there's plagiarizing in source code or not. Traditional detection algorithms cannot fit this condition. The author designed an effective and complete method to detect source code plagiarizing according to the popular way of students' plagiarizing. There are two basic concepts of the algorithm. One is to standardize the source code via filtration against to remove the majority noises intentionally blended by plagiarists. The other one is an improved Longest Common Subsequence algorithm for text matching, using statement as the unit for matching. The authors also designed an appropriate HASH function to increase the efficiency of matching. Based on the algorithm, a system was designed and proved to be practical and sufficient, which runs well and meet the practical requirement in application.

A Comparison of Source Code Plagiarism Detection Engines

2004 ◽  
Vol 14 (2) ◽  
pp. 101-112 ◽  
Author(s):  
Thomas Lancaster ◽  
Fintan Culwin
Keyword(s):  
Source Code ◽  
Plagiarism Detection

scholarly journals WASTK: A Weighted Abstract Syntax Tree Kernel Method for Source Code Plagiarism Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Deqiang Fu ◽  
Yanyan Xu ◽  
Haoran Yu ◽  
Boyang Yang
Keyword(s):  
Kernel Method ◽  
Source Code ◽  
Detection Methods ◽  
Abstract Syntax ◽  
Plagiarism Detection ◽  
Abstract Syntax Tree ◽  
Syntax Tree ◽  
Tree Kernel ◽  
Document Frequency ◽  
Abstract Syntax Trees

In this paper, we introduce a source code plagiarism detection method, named WASTK (Weighted Abstract Syntax Tree Kernel), for computer science education. Different from other plagiarism detection methods, WASTK takes some aspects other than the similarity between programs into account. WASTK firstly transfers the source code of a program to an abstract syntax tree and then gets the similarity by calculating the tree kernel of two abstract syntax trees. To avoid misjudgment caused by trivial code snippets or frameworks given by instructors, an idea similar to TF-IDF (Term Frequency-Inverse Document Frequency) in the field of information retrieval is applied. Each node in an abstract syntax tree is assigned a weight by TF-IDF. WASTK is evaluated on different datasets and, as a result, performs much better than other popular methods like Sim and JPlag.

scholarly journals Flow Chart Generation-Based Source Code Similarity Detection Using Process Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Feng Zhang ◽  
Lulu Li ◽  
Cong Liu ◽  
Qingtian Zeng
Keyword(s):  
Computer Programming ◽  
Process Mining ◽  
Source Code ◽  
Plagiarism Detection ◽  
Dynamic Features ◽  
Code Obfuscation ◽  
Loop Unrolling ◽  
Similarity Detection ◽  
Flow Charts ◽  
And Function

Source code similarity detection has extensive applications in computer programming teaching and software intellectual property protection. In the teaching of computer programming courses, students may utilize some complex source code obfuscation techniques, e.g., opaque predicates, loop unrolling, and function inlining and outlining, to reduce the similarity between code fragments and avoid the plagiarism detection. Existing source code similarity detection approaches only consider static features of source code, making it difficult to cope with more complex code obfuscation techniques. In this paper, we propose a novel source code similarity detection approach by considering the dynamic features at runtime of source code using process mining. More specifically, given two pieces of source code, their running logs are obtained by source code instrumentation and execution. Next, process mining is used to obtain the flow charts of the two pieces of source code by analyzing their collected running logs. Finally, similarity of the two pieces of source code is measured by computing the similarity of these two flow charts. Experimental results show that the proposed approach can deal with more complex obfuscation techniques including opaque predicates and loop unrolling as well as function inlining and outlining, which cannot be handled by existing work properly. Therefore, we argue that our approach can defeat commonly used code obfuscation techniques more effectively for source code similarity detection than the existing state-of-the-art approaches.

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