A Fuzzy R Code Similarity Detection Algorithm | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Fuzzy R Code Similarity Detection Algorithm

  • Conference paper
Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 444))

Abstract

R is a programming language and software environment for performing statistical computations and applying data analysis that increasingly gains popularity among practitioners and scientists. In this paper we present a preliminary version of a system to detect pairs of similar R code blocks among a given set of routines, which bases on a proper aggregation of the output of three different [0,1]-valued (fuzzy) proximity degree estimation algorithms. Its analysis on empirical data indicates that the system may in future be successfully applied in practice in order e.g. to detect plagiarism among students’ homework submissions or to perform an analysis of code recycling or code cloning in R’s open source packages repositories.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aiken, A.: MOSS (Measure of software similarity) plagiarism detection system, http://theory.stanford.edu/~aiken/moss/

  2. Chilowicz, M., Duris, E., Roussel, G.: Viewing functions as token sequences to highlight similarities in source code. Science of Computer Programming 78, 1871–1891 (2013)

    Article  Google Scholar 

  3. Damerau, F.J.: A technique for computer detection and correction of spelling errors. Communications of the ACM 7(3), 171–176 (1964)

    Article  Google Scholar 

  4. Ferrante, J., Ottenstein, K.J., Warren, J.D.: The program dependence graph and its use in optimization. ACM Trans. Program Lang. Syst. 9(3), 319–349 (1987)

    Article  MATH  Google Scholar 

  5. Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Springer (1994)

    Google Scholar 

  6. Gagolewski, M., Grzegorzewski, P.: Possibilistic analysis of arity-monotonic aggregation operators and its relation to bibliometric impact assessment of individuals. International Journal of Approximate Reasoning 52(9), 1312–1324 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  7. Grabisch, M., Marichal, J.L., Mesiar, R., Pap, E.: Aggregation functions. Cambridge University Press (2009)

    Google Scholar 

  8. Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147–160 (1950)

    Article  MathSciNet  Google Scholar 

  9. Lee, C.Y.: Some properties of nonbinary error-correcting codes. IRE Transactions on Information Theory 4(2), 77–82 (1958)

    Article  Google Scholar 

  10. Levenshtein, I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  11. Liu, C., Chen, C., Han, J., Yu, P.S.: GPLAG: Detection of Software Plagiarism by Program Dependence Graph Analysis. In: Proc. 12th ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining (KDD 2006), pp. 872–881 (2006)

    Google Scholar 

  12. Navarro, G.: A guided tour to approximate string matching. ACM Computing Surveys 33(1), 31–88 (2001)

    Article  Google Scholar 

  13. Prechelt, L., Malpohl, G., Philippsen, M.: Finding plagiarisms among a set of programs with JPlag. Journal of Universal Computer Science 8(11), 1016–1038 (2002)

    Google Scholar 

  14. Prechelt, L., Malpohl, G., Phlippsen, M.: JPlag: Finding plagiarisms among a set of programs. Tech. rep. (2000)

    Google Scholar 

  15. Qu, W., Jia, Y., Jiang, M.: Pattern mining of cloned codes in software systems. Information Sciences 259, 544–554 (2014)

    Article  Google Scholar 

  16. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014), http://www.R-project.org/

  17. Winkler, W.E.: String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage. In: Proc. Section on Survey Research Methods (ASA), pp. 354–359 (1990)

    Google Scholar 

  18. Wise, M.J.: String similarity via greedy string tiling and running Karp-Rabin matching. Tech. rep., Dept. of Computer Science, University of Sydney (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Interdisciplinary PhD Studies Program, Systems Research Institute, Polish Academy of Sciences, Poland

    Maciej Bartoszuk

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Marek Gagolewski

  3. Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662, Warsaw, Poland

    Marek Gagolewski

Authors
  1. Maciej Bartoszuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Gagolewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University Montpellier 2, LIRMM - CNRS UMR 5506, 161, Rue Ada, 34392, Montpellier Cedex 5, France

    Anne Laurent

  2. Institute for Human Factors and Technology Management, IAT, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany

    Oliver Strauss

  3. LIP6, UPMC Univ. Paris 06, CNRS UMR 7606, F-75005, Paris, France

    Bernadette Bouchon-Meunier

  4. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald R. Yager

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Bartoszuk, M., Gagolewski, M. (2014). A Fuzzy R Code Similarity Detection Algorithm. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08852-5_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08851-8

  • Online ISBN: 978-3-319-08852-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation