seminar on the ieee paper titled as "An Approach To Source Code Plagiarism Detection and Investigation using Latent Semantic Analysis"

1. AN APPROACH TO SOURCE CODE
PLAGIARISM DETECTION AND
INVESTIGATION USING LATENT SEMANTIC
ANALYSIS
Authors: Georgina Cosma and Mike Joy
Presented by
Varsha Bhat K(1DS09CS105)

2. INTRODUCTION
 Source code plagiarism: reuse of source code
authored by someone else & fail to adequately
acknowledge the fact
 It may occur intentionally or unintentionally
 Used by higher education academics

3. CHALLENGES INVOLVED
 Detect similar file pairs
 Investigating the similar source code fragments
within the detected files
 Determine if the similarity is suspicious or innocent
 Burden of proof:-
“Not only do we need to detect instances of
plagiarism, we must also be able to demonstrate
beyond reasonable doubt that those instances are
not chance similarities.”

4. EXISTING TOOLS
Category of the tools:
 Fingerprint based systems
 String matching systems
 Parameterized matching systems
These were identified by Mozgovoy

5. FINGER PRINT BASED SYSTEM
 Create finger print for each of the files
 Finger print contains statistical information
 There are various metrics used for detecting
plagiarism. Ex: Halstead‟s metrics
 Example of such a system is the ITPAD

6. STRING MATCHING APPROACH
The various steps involved here are :--
 Stage I is the process of tokenization
 Then the source code is written as a series of token
strings
 Tokens are compared to check for similarity
 Example tools are:
 MOSS
 YAP3
 JPLAG

7. PARAMETERIZED MATCHING SYSTEMS
 Detects identical and near duplicate sections of
source code
 Achieved by matching source code sections whose
identifiers have been substituted for systematically
 Ex: DUP tool
INFORMATION RETRIEVAL METHODS
 Represents program as indexed set of keywords
 Computed the frequency of these keywords
 Then computed the pair wise similarity
 Ex: PDetect

8. PLAGATE
 Detect similar source code files
 Investigate the similar code fragments within them
 The view of investigation is to gather evidence for
proving plagiarism by indicating contribution levels
of fragments
 This enhances detection performance of existing
algorithms
 Uses the technique of Latent Semantic Analysis to
achieve this

9. LATENT SEMANTIC ANALYSIS
 It is an information retrieval technique
 Text collection is preprocessed a11 a12
 Represented as a term-by-file matrix a21 a22
 Matrix transformation is applied
 Singular value decomposition performed
 Thus uncovers latent relationships
 Derives meaning of terms by approximating the
structure of term usage among document using
SVD

10. ADVANTAGES
 Can detect transitive relationships unlike the
traditional text retrieval systems
 Helps reduce noise in the data
 Overcomes problems of synonymy and polysemy
 Changes to document structure will not affect the
detection
 Language independent
DISADVANTAGES
 Gives relatively high similarity values for non copied
programs also

11. SIMILARITY IN SOURCE CODE FILES
 Key factors for judging similarity in files are
 Nature of programming language and the problem
 Variance in solution
 Supporting source code already given
 Assignment requirements
 Fragments under investigation must not be
 Short
 Simple
 Standard
 Trivial
 Limited functionality
 Frequently published

12. SIMILARITY CATEGORIES
 Source code fragments have varying contribution to
evidence for plagiarism
 Thus arises the need for a criterion for identifying
the contributions
 Contribution levels
1. Contribution level 0- no contribution
2. Contribution level 1- low contribution
3. Contribution level 2- high contribution
 Similarity levels
1. Level 0- innocent
2. Level 1- suspicious

13. PLAGATE SYSTEM
 Aim: enhance the process of plagiarism detection
and investigation
 It is integrated with external detection tools as an
enhancer
 Components
1. PlaGate Detection tool (PGDT)
2. PlaGate Query tool (PGQT)

14. FUNCTIONALITY

15. SYSTEM REPRESENTATION
 File copus C
C={ F1, F2, …….. Fn }
 Source code fragment „s‟ from source code file F
FɛC
F= { s1, s2, ………sp }
 Set of source code fragments S
 File length ‘lf’ where
 Source code fragment length ‘ls’

16. LSA PROCESS IN PLAGATE
 Preprocess the files
 Transform the corpus of files into an m x n matrix
A=[ ]
 Term weighting algorithm are applied to them
value of term in file:
 SVD is performed on the weighted matrix A
 Reduction of dimention

17. DETECTION AND CLASSIFICATION PROCESS IN
PLAGATE
 PGQT component transforms the input file or
fragment into a query vector „q‟
 Then q is projected onto the k-dimensional space
 Thus we get:
 We now measure similarity between Q and all the
source code files in the corpus by using similarity
measure
 Cosine similarity measure is the most popular

18. EXPERIMENTATION
 Four corpora consisting of java source code files
 Corpora is produced by undergraduate students at
University of Warwick
 Students were given simple skeleton code to start
with
The Data Sets

19. PERFORMANCE EVALUATION MEASURES
 sim(Fa,Fb) gives the similarity of two files and is
computed using similarity measure
 Recall and Precision are two most commonly used
measures for information retrieval systems
 A threshold is selected Ø
 Files that have sim(Fa,Fb) ≥ Ø are detected

20.  Overall performance will be evaluated by combining
both the measures
 Closer the value of F to 1.00 the better is the
detection performance

21. PLAGATE VS JPLAG AND SHERLOCK
 Performance when tools function alone and when
integrated with PlaGate is evaluated
 List of suspicious file are created

22.  Results
 Recall increases after integration with PGDT
 This constant increase indicates PGDT and
external tools compliment each other
 Further increase seen when both PGDT and
PGQT are integrated but at the cost of Precision

23.  JPlag alone had high Precision and low
Recall in all data sets
 Sherlock and JPlag, both string matching
algorithms vary significantly in detection
performance

25.  Similarity often occurs in groups containing more
than 2 files
 JPlag and Sherlock fail to parse some suspicious
files due to Local Confusion
 Local Confusion occurs when some code segments
shorter than the minimum- match length have been
shuffled in files as they are string matched
algorithms
 PlaGate does not suffer from this sort of local
confusion as it does not depend on the structure of
the code

26. Example

27. CONCLUSION
 LSA based technique for plagiarism detection and
investigation as enhancers
 Detection of missed source code files by current
plagiarism detection tools
 Integration with PlaGate increases Recall at the
cost of Precision
 Classification of similarity by PlaGate into
contribution levels
 PlaGate is language independent
 Unlike other tools that find the similarity of two
files, PlaGate finds the relative similarity

28. FUTURE WORK
 Automating dimensionality reduction is still a
problem
 Miss classification of source code fragment
 PlaGate behavior is not as stable as the string
matching algorithms