simple techniques for plagiarism detection in student programming projects szymon grabowski,...

Simple techniques for plagiarism detection

in student programming projectsSzymon Grabowski, Wojciech Bieniecki

Computer Engineering Dept., Tech. Univ. of Łódź, Poland

{SGrabow, WBieniec}@kis.p.lodz.pl

Sieci i Systemy Informatyczne, Łódź, październik 2006

We plagiarized it...

2Sz. Grabowski & W. Bieniecki, Simple techniques for plagiarism detection...

http://library.kcc.hawaii.edu/main/images/plagiarism_cartoon.gif

What is plagiarism?

3

Plagiarism everywhere

Sz. Grabowski & W. Bieniecki, Simple techniques for plagiarism detection...

• text (articles, scientific papers (also self-plagiarisms), essays

...or just plot ideas in fiction books)

• music (melodies, “sampling”)

• images (copy/paste e.g. from web pages)

Our interest: text plagiarism.

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Text plagiarism

• stealing natural language (NL) texts

• stealing software code


5

Previous work (1/3)

Faidhi & Robinson (1987): six levels of program modification in a plagiarism attempt:

(i) changing comments, (ii) changing identifier names,

(iii) reordering variable positions, (iv) procedure combination,

(v) changing program statements, (vi) changing control logic.

Changes in the program control logic are most laborious (and vulnerable to hard-to-detect errors) but also hardest to properly identify as plagiarism.


6

Previous work (2/3)

Irving (2004): finding local similarity with a variant of

the Smith-Waterman classic algorithm (1982). Aim: taking care of both precision/recall and speed.

Pretchelt et al. (2000):JPlag online system.

Basic technique: find a set of indentical substrings of strings A and B, adhering to a few simple rules.

Quite robust to reordering parts of the text.


7

Previous work (3/3)

Many algorithms based on various code complexity measures

(like e.g. the number of execution paths through a program). (See [Clough, 2000] for details.)

Mozgovoy et al. (2005):Suffix array based alg.

to decrease the computation complexity ofall-against-all file comparison.


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Our motivation

Some say that laziness is a professional feature...

Therefore we wanted to keep things simple(as opposed to many algs from the literature).

Our task: find plagiarisms in student homeworks.Namely, in Java projects.

Small projects: not more than a few hundred lines expected.


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We conjecture that the relative order and frequency of the keywords of a given language

is quite a good indicator if two documents were created independently or not.

Because it is not easy to find synonymous constructs without some understanding of the code.

Our approach

Why keywords? Maybe operators instead?

Rather not. Examples (in C and similar lang.): x = y / 2; x = y * 0.5;

x-=2; x--, x--;


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Java keywords


http://java.sun.com/docs/books/tutorial/java/nutsandbolts/_keywords.html

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Three variantsExtracting keywords, that’s easy. What then?

What similarity measure?

We propose 3 variants:

• based on the context-free counts of the keywords, i.e., order-0 statistics;

• based on the similarity of the statistics of pairs of successive keywords in the source files, i.e., order-1 rather than order-0 statistics;

• based on the similarity between the whole sequences of used keywords, in the order of their appearances, with aid of

the LCS (longest common subsequence) measure.


In all variants we measure pair-wise file similarity.

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Algorithm I (order-0 statistics)1. For both files we create a dictionary (Dict1 and Dict2,

respectively) of occurring keywords with the number of occurrences (a histogram).

2. We calculate the total number of keywords C.

2121

1\22

2\11

;maxDictDictk

DictDictkDictDictk

kcountkcount

kcountkcountC

3. We calculate the number of keyword repetitions R:

21

21 ;maxDictDictk

kcountkcountR

4. We evaluate the similarity S = R / C.


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Algorithm II (order-1 statistics)

1. For both files create a sequence of keywords (List1 and List2).

2. For each element i of List1 (except from the last one) take its successor List1(i+1) and add the pair to the list lp1. Delete the repeated records from lp1.

3. Analogously for lp2.

4. Evaluate the similarity measure S:

2,1min

21

lplp

lplpS


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Given strings A, B, |A| = n, |B| = m, find the longest subsequence shared by both strings.

Sometimes we are interested in a simpler problem:finding only the length of the LCS (LLCS), not the

matching sequence.

Longest Common Subsequence (LCS)

A = m a t t e rB = b r o t h e r s

LLCS(A, B) = 3.

LCS(A, B) = t e r

LCS example


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Algorithm IIILCS on strings where the “characters”

are keywords


1. Denote the sequence of keywords in file1 and file2with Word1, Word2, respectively.

2. Use the formula for similarity measure S:

2,1min

2,1

WordWord

WordWordLCSS

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Implementation / test setup

All codes in Python 2.4(perfect language for reluctant coders).

Test machine: Pentium4 3 GHz, 512 MB of RAM, Windows XP SP2.

Input files:15 student Java projects (single source files)

solving the same task: displaying time on an analog clock, using a client-server technology

(a server provides the system time, and many clients can be connected to the server).


17Sz. Grabowski & W. Bieniecki, Simple techniques for plagiarism detection...

Test files

Files that in fact are

plagiarisms are in the positions:

5 → 1, 8 → 15, 7 → 10

and 7 → 13.

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Alg I (order-0 stats), similarity measure


...But the 4th thief was not

detected.

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Alg II (order-1 stats), similarity measure


Not perfectbut not

bad either...

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Alg III (LCS), similarity measure


Allfour

thieves at the

top!

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Conclusions


All the presented algorithms seem to indicate the plagiarized codes properly.

But in practice it is impossible to set the threshold similarity value for each algorithm above which the files are plagiarisms.

In Algorithm I the values of similarity vary from 0.75 to 1 and all below 0.98 don’t indicate a plagiarism.

This algorithm is the most resistant to changing the order of instructions and functions.

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Algorithm II is pretty resistant to changing the order of functions and blocks of instructions.

The range of obtained similarity measure values is much wider comparing to the first case.

Algorithm III, based on the LCS measure, is vulnerable to changing the order of functions and instructions in the file.

In the inspected case, however, students stealing the code did not bother to mix the functions

so the results are comparable to Algorithm II.

Conclusions, cont’d

All presented algorithms should work properly if a stolen homework is only a part of the original code.


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Future plans


Making it more robust to function reordering(even Algorithm II).

Idea: convert a source file to a cannonical form,sorting functions according to their signatures.

More experiments (also for sources in C++, PHP...).

Handling multi-file projects.

Use not only keywords but standard library function names too?

Several independent similarity measures and the detection based on training?

simple techniques for plagiarism detection in student programming projects szymon grabowski,...

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