Quarterly journal published in SPbPU
and edited by prof. Dmitry Zegzhda
and edited by prof. Dmitry Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
ISSN 2071-8217
DETECTING SOURCE CODE FRAGMENTS SIMILARITY WITH MACHINE LEARNING ALGORITHMS
N. A. Gribkov, T. D. Ovasapyan, D. A. Moskvin Peter the Great St. Petersburg Polytechnic University (SPbPU)
Annotation: The paper proposes a method for detecting source code fragments similarity using attribute abstract syntax trees and machine learning algorithms. The advantages of the method are determined based on a comparative analysis of existing approaches of detecting code clones. For approaches, which use AST, it is possible to increase the efficiency of detecting similar source code fragments by detecting semantic clones with usage of method proposed.
Keywords: code clones, syntactic similarity, semantic similarity, open-source software.
Pages 62-71