A Fuzzy-based approach to programming language independent source-code plagiarism detection

Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) S. 1 - 8
Hauptverfasser: Acampora, Giovanni, Cosma, Georgina
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2015
Schlagworte:
Clustering algorithms
Java
Measurement
Plagiarism
Prediction algorithms
Software algorithms
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.
AbstractList Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.
Author Cosma, Georgina
Acampora, Giovanni
Author_xml – sequence: 1
  givenname: Giovanni
  surname: Acampora
  fullname: Acampora, Giovanni
  email: giovanni.acampora@ntu.ac.uk
  organization: Sch. of Sci. & Technol., Nottingham Trent Univ., Nottingham, UK
– sequence: 2
  givenname: Georgina
  surname: Cosma
  fullname: Cosma, Georgina
  email: georgina.cosma@ntu.ac.uk
  organization: Sch. of Sci. & Technol., Nottingham Trent Univ., Nottingham, UK
BookMark eNotj7FqwzAURVVohzbNFxSKfsCuZFmWNIZgt4FAlmZJh_AsPbsCWzK2MyRfX0Oz3HuGy4H7Qh5DDEjIO2cp58x8VMfTKdmVZZlmjMtUCaGMkA9kbZTmeaGEyjNdPJOfDa0ut9s1qWFCR2EYxgj2l86RLtSO0Pc-tLSD0F6gReqDwwGXCDOd4mW0mNjokA4dtB5GP_XU4Yx29jG8kqcGugnX916RY1V-b7-S_eFzt93sE88zMSe2YbK2OThZZNwyFLnjrHYFNoXlAhqbKyWkBiWRcb1sdcNRGWOYc6CdEivy9u_1iHgeRt_DeD3fL4s_b1dTJQ
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/FUZZ-IEEE.2015.7337935
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781467374286
1467374288
EndPage 8
ExternalDocumentID 7337935
Genre orig-research
GroupedDBID 6IE
6IH
CBEJK
RIE
RIO
ID FETCH-LOGICAL-i123t-cf05bc4ad5621c0e34d10bd6ef6c13afc477358a75e01805b8f1e79990dda8d73
IEDL.DBID RIE
IngestDate Thu Jun 29 18:37:28 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i123t-cf05bc4ad5621c0e34d10bd6ef6c13afc477358a75e01805b8f1e79990dda8d73
PageCount 8
ParticipantIDs ieee_primary_7337935
PublicationCentury 2000
PublicationDate 20150801
PublicationDateYYYYMMDD 2015-08-01
PublicationDate_xml – month: 08
  year: 2015
  text: 20150801
  day: 01
PublicationDecade 2010
PublicationTitle 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublicationTitleAbbrev FUZZ-IEEE
PublicationYear 2015
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7003974
Snippet Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments....
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Clustering algorithms
Java
Measurement
Plagiarism
Prediction algorithms
Software algorithms
Title A Fuzzy-based approach to programming language independent source-code plagiarism detection
URI https://ieeexplore.ieee.org/document/7337935
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEB3a4sGTSit-k4NH02abzSZ7FHHxVHqwUOqh5FMW7LbUbcH-epPdbUXw4i2EQGASeJPJe_MA7mWilOaCYRYb7h8ojGDBRIKl9nDGLRkqriuzCT4aiek0Hbfg4aCFsdZW5DPbD8PqL98s9SaUygacUn-dWBvanCe1VqsR_UYkHWST2QyHJ1IgbLF-s_iXa0oFGtnJ_7Y7hd6P-g6ND7hyBi1bdOHtEWWb3e4LB9gxaN8KHJVL1FCsFn4x2pcfUX6wty1RXaDHQb2OVh_yPQ_GgwtkbFnxsIoeTLLn16cX3Bgj4NwDTYm1I0zpWBqfvESaWBqbiCiTWJfoiEqnY84pE5IzG_pzMSVcZLlPBYkxUhhOz6FTLAt7AcgpJ4aWauXzmDh1ShIXkjwipUtFQugldENg5qu698W8icnV39PXcBxiXxPkbqBTrjf2Fo70tsw_13fVgX0D7W6a-w
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxFAy1CnpSacVvc_Bo2myz2WSPIi4Va-mhhVIPJZ-yYLelbgX76012txXBi7cQAoEkMO-9zLwB4FZEUirGKaKhZi5BoRhxyiMklIMzZnBHMlWYTbB-n4_H8aAG7rZaGGNMQT4zLT8s_vL1XK18qazNCHHPie6AXRqGHVyqtSrZb4DjdjKaTJBPkjxli7aq5b98UwrYSA7_t-ERaP7o7-BgiyzHoGayBni9h8lqvf5CHng03DQDh_kcViSrmVsMNwVImG4NbnNYluiR16_Dxbt4S7314AxqkxdMrKwJRsnj8KGLKmsElDqoyZGymEoVCu3Cl0BhQ0IdYKkjYyMVEGFVyBihXDBqfIcuKrkNDHPBINZacM3ICahn88ycAmil5R1DlHSRTBhbKbD1YR4WwsY8wuQMNPzBTBdl94tpdSbnf0_fgP3u8KU37T31ny_Agb-Hki53Cer5cmWuwJ76zNOP5XVxed8oXp5C
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2015+IEEE+International+Conference+on+Fuzzy+Systems+%28FUZZ-IEEE%29&rft.atitle=A+Fuzzy-based+approach+to+programming+language+independent+source-code+plagiarism+detection&rft.au=Acampora%2C+Giovanni&rft.au=Cosma%2C+Georgina&rft.date=2015-08-01&rft.pub=IEEE&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FFUZZ-IEEE.2015.7337935&rft.externalDocID=7337935