1. Guide : Ms Sangeetha Jamal Presented by
Dept of Computer Science Merin Paul
Mtech CS-IS S1
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2. Contents
 Introduction
 Types of Source-code Plagiarism
Textual Similarity
Functional Similarity
 Source Code Detection Algorithms.
 Detecting Techniques
 Tools used for code based plagiarism.
 Conclusion
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3. Introduction
Plagiarism in source-code files occurs when source-code
is copied and edited without proper acknowledgment of
the original author.
Techniques for plagiarism: Lexical changes and structural
changes.
Lexical changes: changes that can be done to the source-
code without affecting the parsing of the program
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4. Introduction
Structural changes: changes made to the source code that
will affect the parsing of the code and involve program
debugging.
Reasons for code copying:
Code reusing.
Programmer limitation
Coincidentally implement using the same logic
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5. TYPES OF SOURCE CODE
PLAGIARISM
 Textual Similarity
 Functional Similarity
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6. Textual Similarity
 Two individual source codes look similar based on their
textual content.
 Textual content mean the words, letters, variable
names, etc
 Type 1, Type 2, Type 3.
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7. Type I
 The copied code fragment is as same as the original one
without any modification except white spaces, comments
and line modifications.
int a; // counter
// count five times
for(a = 0; a < 5; a++)
{
printf(“a = %d”, a); // print value of a
}
return 0;
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8. Type I
int a;
/* Loop increasing of a and print a value of it */
for(a = 0; a < 5; a++){
printf(“a = %d”, a);
}
return 0;
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9. Type II
 Same as Type I and also with modifications to variable
names, function names and other user-defined identifiers.
if(a > b)
{
a = a - 1;
b = b * a; // comment 1
}
else
{
b = a; // comment
2a = 0;
}
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10. Type II
if(m > n)
{m=m - 5;
n=n*m; //my comment 1
}
else
{n=m; //my comment
2m=0;
}
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11. Type III
 A copied code fragment is done by inserting or
removing unnecessary statements.
if(a > b)
{
a = a - 1;
b = b * a;
}
else
{
b = a;
a = 0;
}
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12. Type III
if(a > b)
{
a = a – 1;
c = 0; // this statement is added
b = b * a;
}
else
{
b = a;
a = 0;
}
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13. Functional similarity
It refers to the code fragments that have the same semantic or
functionality.
fragment 1 : fragment 2:
int i , j = 1; int factorial(int n)
for(i = 1; i <= VALUE; i++) {
j = j * i; if(n == 0) return 1;
else return factorial(n – 1)*n;
}
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14. Source Code Detection Algorithms
 Text based
 Token-based
 Parse tree-based
 PDG-based
 Metrics-based
 Hybrid Approaches
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15. CONTD..
 Text based
 Find
textual match between two source codes..
Simple and Fast.
 Token based
 Using a lexer to convert the program into tokens.
Find a match in token sequences.
More robust to simple text replacements.
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16. CONTD…
 Parse Trees
Build and compare parsetrees
Contains the complete information about the
source code
Tree comparison can normalize conditional
statements.
 Program Dependency Graphs (PDGs)
Captures the actual flow of control in a program.
Allows higher-level equivalences to be located.
More complex.
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17. CONTD…
 Metrics
capture 'scores' of code segments according to
certain criteria.
Metrics are simple to calculate.
Lead to false positives.
• Hybrid
Combination of two or more previous
techniques.
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18. Detecting Techniques
Detection via Lexical Similarities
The process of lexical analysis takes source code and
converts it into a stream of lexical tokens.
Source code undergoes a series of transformation.
Identification of reserved words, identifiers, and
numbers are beneficial for plagiarism detection.
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19. CONTD…
int[] A = {1,2,3,4}; int[] B = {1, 2, 3, 4};
for(int i = 0; i < for(int j = 0; j < B.length;
A.length; i++) { j++) {
A[i] = A[i] + 1; B[j] = B[j] + 1;
} }
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20. CONTD…
LITERAL_int LBRACK RBRACK IDENT ASSIGN
LCURLY NUM_INT COMMA NUM_INT
COMMA NUM_INT COMMA NUM_INT RCURLY SEMI
LITERAL_for LPAREN LITERAL_int IDENT ASSIGN
NUM_INT SEMI IDENT LT
IDENT DOT IDENT SEMI IDENT INC RPAREN LCURLY
NUM_INT SEMI
RCURLY
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21. Detection via Parse Tree Similarities
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22. Detection via Metrics
 Calculate and compare attribute counts.
 Programs with similar attribute counts are potentially
similar programs.
 Counts of operators and operands are typically used to
construct attribute counts.
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23. Tools used for code based plagiarism
Jplag
 Finds similarities among multiple sets of source code files.
 JPlag operates in two phases.
 First phase: All programs to be compared are parsed and
converted into token strings.
 Second phase: Token strings are compared in pairs for
determining the similarity of each pair.
 It is more robust. It supports Java, c#, C, C++ and natural
language text.
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24. CONTD..
MOSS (Measure Of Software Similarity)
 Measure Of Software Similarity was developed in 1994
by Alex Aiken.
 It analyzes code written in languages like
C, C++, Python, Visual
Basic, Javascript, FORTRAN, Lisp, Ada etc.
 Provided as an internet service and given a list of source
files.
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25. CONTD…
 YAP (Yet Another Plague)
 Token-based system.
 YAP works in two phases.
 The first phase generates a token file for each submission.
 The second phase compares pairs of token files using the
token matching algorithm, Running-Karp-Rabin Greedy-
String-Tiling algorithm (RKRGST)
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26. Conclusion
 Plagiarism in programming assignments is an inevitable
issue for most academics teaching programming.
 Plagiarism Detection systems are built based on a few
languages.
 Most of the detection software checking is done with
some repository situated in an organization.
 As the number of digital copies are going up the
repository size should be large and the plagiarism
Detection software should be able to handle it.
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27. Conclusion
 Plagiarism in programming assignments is an inevitable
issue for most academics teaching programming.
 Most popular plagiarism detection algorithms use string-
matching to create token string representations of
programs.
 The tokens of each document are compared on a pair-wise
basis to determine similar source-code segments between
the files.
 String-matching systems are language-dependent
depending on the programming languages supported by
their parsers
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28. References
1) G. Cosma and M. Joy,” An Approach to Source-Code Plagiarism
Detection and Investigation Using Latent Semantic Analysis”
IEEE Trans. Computers, vol. 61, no. 3, pp. 379-391, March 2012
2) Georgina Cosma, Mike Joy, Daniel White and Jane Yau, 9th
August 2007 ,ICS,University of Ulster
http://www.ics.heacademy.ac.uk/resources/assessment/plagiarism/
3) Okiemute Omuta ”Electronic Source Code Plagiarism Detection”
Computer Engineering Department,European University of
Lefke, North Cyprus
4) S. Schleimer, D. Wilkerson, and A. Aiken, “Winnowing: Local
Algorithms for Document Fingerprinting,” Proc. the ACM
SIGMOD Int’l Conf. Management of Data, pp. 76-85, 2003
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29. References
4) M.J. Wise, “YAP3: Improved Detection of Similarities in Computer
Program and Other Texts,” Proc. 27th SIGCSE Technical
Symp., pp. 130-134, 1996.
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30. THANK U!!!
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