1. Source Code and Text Plagiarism Detection Strategies
Maeve Paris
University of Ulster

2. Summary
Distinction drawn between plagiarism of text and plagiarism of source code
Different tools and metrics have been developed for each
BUT if we consider computer programming languages as being similar to natural languages,
We might be able to apply techniques from computer-assisted text analysis to detect source code plagiarism/ collusion

3. Plagiarism detection in text
Verbatim copying
Paraphrasing
Inconsistencies
Authorship attribution: CATA tools for Concordances
KWIC index
Online services (plagiarism.org, WordCHECK, etc).

4. KWIC and Frequency lists

5. Plagiarism detection in source code
Metrics-driven
Plagiarising transformations
Lexico-structural approach
Sim
Yap
MOSS
JPLAG

6. Is source code free speech?
Statement in English:
The conference session starts at nine.
Statement in Java:
Dog dog = new Dog();
dog.bark();
Statement in Prolog:
listensToMusic(yolanda):- happy(yolanda).
s --> np, vp.
Syntax and lexicon in all cases

7. Authoring styles in source code
Choice of algorithm
Naming of classes and variables
Physical layout
Comments

8. Analysis
Corpus of 35 java files
Manual inspection
JPLAG
Concordance

9. JPLAG

10. JPLAG

11. JPLAG

12. Concordance

13. Stop list

16. Conclusions
Computer programming languages are similar to natural languages
It is possible to distinguish a programming style
CATA may help in detecting plagiarism
Focus on control structures may lead to wrong conclusions

17. Conclusions CATA is not restricted to programs which compile
Need for tools to combine metrics-based profiles with concordancing features