Source code similarity detection by using data mining methods

Programming courses at university and high school level, and competitions in informatics (programming), often require fast assessment of received solutions of the programming tasks. This problem is usually solved by use of automated systems that check the produced output for some test cases for ever...

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Published in:2013 35th International Conference on Information Technology Interfaces (ITI) pp. 257 - 262
Main Authors: Stankov, Emil, Jovanov, Mile, Bogdanova, Ana Madevska
Format: Conference Proceeding
Language:English
Published: SRCE University Computing Centre, University of Zagreb 01.06.2013
Subjects:
Algorithm design and analysis
clustering analysis
code similarity
Data mining
Educational institutions
evaluation of source code
Informatics
Programming code
Programming profession
Vectors
ISBN:9789537138301, 9537138305
ISSN:1334-2762
Online Access:Get full text
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Abstract Programming courses at university and high school level, and competitions in informatics (programming), often require fast assessment of received solutions of the programming tasks. This problem is usually solved by use of automated systems that check the produced output for some test cases for every solution. In our paper we present a novel approach of representation of the programming codes as vectors, and use of these vectors in data mining analysis that could produce better assessment of the solutions. We present the results of cluster analysis that go up to 88% of correctly clustered items on average.
AbstractList Programming courses at university and high school level, and competitions in informatics (programming), often require fast assessment of received solutions of the programming tasks. This problem is usually solved by use of automated systems that check the produced output for some test cases for every solution. In our paper we present a novel approach of representation of the programming codes as vectors, and use of these vectors in data mining analysis that could produce better assessment of the solutions. We present the results of cluster analysis that go up to 88% of correctly clustered items on average.
Author Jovanov, Mile
Stankov, Emil
Bogdanova, Ana Madevska
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  givenname: Mile
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  givenname: Ana Madevska
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  fullname: Bogdanova, Ana Madevska
  email: ana.madevska.bogdanova@finki.ukim.mk
  organization: Fac. of Comput. Sci. & Eng., Univ. Ss. Cyril & Methodius, Skopje, Macedonia
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SubjectTerms Algorithm design and analysis
clustering analysis
code similarity
Data mining
Educational institutions
evaluation of source code
Informatics
Programming code
Programming profession
Vectors
Title Source code similarity detection by using data mining methods
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