Hybridizing evolutionary algorithms and clustering algorithms to find source-code clones

Andrew Sutton; Huzefa Kagdi; Jonathan Maletic; L Volkert

doi:10.1145/1068009.1068191

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Hybridizing evolutionary algorithms and clustering algorithms to find source-code clones

Andrew Sutton, Huzefa Kagdi, Jonathan Maletic and L Volkert

Proceedings of the 7th annual conference on genetic and evolutionary computation, pp.1079-1080

GECCO '05

06-25-2005

DOI: https://doi.org/10.1145/1068009.1068191

Abstract

clone detection

evolutionary algorithms

software engineering

This paper presents a hybrid approach to detect source-code clones that combines evolutionary algorithms and clustering. A case-study is conducted on a small C++ code base. The preliminary investigation indicates that such an approach is effective in detecting groups of source-code clones.

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Title: Hybridizing evolutionary algorithms and clustering algorithms to find source-code clones
Creators: Andrew Sutton - Kent State University
Huzefa Kagdi - Kent State University
Jonathan Maletic - Kent State University
L Volkert
Publication Details: Proceedings of the 7th annual conference on genetic and evolutionary computation, pp.1079-1080
Series: GECCO '05
Publisher: ACM
Identifiers: 99383436511706570
Academic Unit: U.A. Whitaker College of Engineering
Language: English
Resource Type: Conference proceeding

Hybridizing evolutionary algorithms and clustering algorithms to find source-code clones

Abstract

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