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Exploring the Use of Genetic Algorithm Clustering for Mobile App Categorisation

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Search-Based Software Engineering (SSBSE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12420))

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Abstract

Search-based approaches have been successfully used as clustering algorithms in several domains. However, little research has looked into their effectiveness for clustering tasks commonly faced in Software Engineering (SE). This short replication paper presents a preliminary investigation on the use of Genetic Algorithm (GA) to the problem of mobile application categorisation. Our results show the feasibility of GA-based clustering for this task, which we hope will foster new avenues for Search-Based Software Engineering (SBSE) research in this area.

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Notes

  1. 1.

    http://clapp.afnan.ws/data/.

  2. 2.

    Modified GAMA code can be found here: https://github.com/afnan-s/gama.

  3. 3.

    The cluster centre is the arithmetic mean of all the points belonging to the cluster.

  4. 4.

    Running time for GAC with k = 24, population = 500, generations = 1000.

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Author information

Authors and Affiliations

  1. CCIS, King Saud University, Riyadh, Saudi Arabia

    Afnan A. Al-Subaihin

  2. University College London, London, UK

    Federica Sarro

Authors
  1. Afnan A. Al-Subaihin
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  2. Federica Sarro
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Corresponding author

Correspondence to Afnan A. Al-Subaihin .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Aldeida Aleti

  2. Delft University of Technology, Delft, The Netherlands

    Annibale Panichella

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Al-Subaihin, A.A., Sarro, F. (2020). Exploring the Use of Genetic Algorithm Clustering for Mobile App Categorisation. In: Aleti, A., Panichella, A. (eds) Search-Based Software Engineering. SSBSE 2020. Lecture Notes in Computer Science(), vol 12420. Springer, Cham. https://doi.org/10.1007/978-3-030-59762-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-59762-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59761-0

  • Online ISBN: 978-3-030-59762-7

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