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A bi-level evolutionary approach for the multi-label detection of smelly classes

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Lamjed Ben SaidAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 782 - 785

https://doi.org/10.1145/3520304.3528946

Published: 19 July 2022 Publication History

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Abstract

This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.

References

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Radhia Azzouz, Slim Bechikh, and Lamjed Ben Said. 2014. A Multiple Reference Point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes. In 2014 IEEE Congress on Evolutionary Computation (CEC). 3168--3175.

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Cited By

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Hadj-Kacem MBouassida N(2024)Multi-label learning for identifying co-occurring class code smellsComputing10.1007/s00607-024-01294-x106:8(2585-2612)Online publication date: 27-May-2024
https://doi.org/10.1007/s00607-024-01294-x
Dewangan SRao RChowdhuri SGupta M(2023)Severity Classification of Code Smells Using Machine-Learning MethodsSN Computer Science10.1007/s42979-023-01979-84:5Online publication date: 29-Jul-2023
https://dl.acm.org/doi/10.1007/s42979-023-01979-8

Index Terms

A bi-level evolutionary approach for the multi-label detection of smelly classes
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
    2. Software post-development issues
      1. Maintaining software

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Published In

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 19 July 2022

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Cited By

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Hadj-Kacem MBouassida N(2024)Multi-label learning for identifying co-occurring class code smellsComputing10.1007/s00607-024-01294-x106:8(2585-2612)Online publication date: 27-May-2024
https://doi.org/10.1007/s00607-024-01294-x
Dewangan SRao RChowdhuri SGupta M(2023)Severity Classification of Code Smells Using Machine-Learning MethodsSN Computer Science10.1007/s42979-023-01979-84:5Online publication date: 29-Jul-2023
https://dl.acm.org/doi/10.1007/s42979-023-01979-8

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