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A fuzzy model for high-level clones in software

Authors:

Niyati Baliyan,

Vidushi Sharma,

ShivaniAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 38, Issue 3

Pages 1 - 4

https://doi.org/10.1145/2464526.2464531

Published: 23 May 2013 Publication History

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Abstract

High-level similarities in software, referred to as High-Level Clones (HLCs) may manifest themselves as behavior clones, concept clones, collocated simple clones or domain model clones [3]. The presence of such similarities may deter the maintainability of software, while identifying them may improve the reuse, reengineering, evolution and understandability of software [2]. This study proposes an integrated measure of high-level cloning, using a four input fuzzy model. It also proves that the calculated extent of high-level cloning is practically equal to the simulation result in the MATLAB's fuzzy toolbox.

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Baliyan Niyati and Sharma Vidushi. 2012. A Framework for Mining High Level Clones. International Journal of Advanced Research in Computer Science and Software Engineering. 2(5), 25--29.

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Baliyan NKumar S(2013)Quality Assessment of Software as a Service on Cloud Using Fuzzy Logic2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)10.1109/CCEM.2013.6684439(1-6)Online publication date: Oct-2013
https://doi.org/10.1109/CCEM.2013.6684439

Index Terms

A fuzzy model for high-level clones in software
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

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cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 38, Issue 3

May 2013

94 pages

ISSN:0163-5948

DOI:10.1145/2464526

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Association for Computing Machinery

New York, NY, United States

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Published: 23 May 2013

Published in SIGSOFT Volume 38, Issue 3

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Baliyan NKumar S(2013)Quality Assessment of Software as a Service on Cloud Using Fuzzy Logic2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)10.1109/CCEM.2013.6684439(1-6)Online publication date: Oct-2013
https://doi.org/10.1109/CCEM.2013.6684439

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Identifying Similar Software Datasets through Fuzzy Inference System

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A Data Mining Approach for Detecting Higher-Level Clones in Software

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