Skip to main content
SpringerLink
Log in

Exploring the Use of Genetic Algorithm Clustering for Mobile App Categorisation

  • Conference paper
  • First Online:
Search-Based Software Engineering (SSBSE 2020)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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.

References

  1. Al-Subaihin, A., Sarro, F., Black, S., Capra, L.: Empirical comparison of text-based mobile apps similarity measurement techniques. EMSE 24(6), 3290–3315 (2019)

    Google Scholar 

  2. Al-Subaihin, A.A., et al.: Clustering mobile apps based on mined textual features. In: ESEM 2016 (2016)

    Google Scholar 

  3. AlSubaihin, A., Sarro, F., Black, S., Capra, L., Harman, M.: App store effects on software engineering practices. IEEE TSE (2019). https://doi.org/10.1109/TSE.2019.2891715

  4. Auch, M., Weber, M., Mandl, P., Wolff, C.: Similarity-based analyses on software applications: a systematic literature review. JSS 168 (2020). https://doi.org/10.1016/j.jss.2020.110669. Article No. 110669

  5. Ceccato, M., Falcarin, P., Cabutto, A., Frezghi, Y.W., Staicu, C.A.: Search based clustering for protecting software with diversified updates. In: SSBSE 2016 (2016)

    Google Scholar 

  6. Doval, D., Mancoridis, S., Mitchell, B.: Automatic clustering of software systems using a genetic algorithm. In: Proceedings of STEP 1999. IEEE Computer Society (1999)

    Google Scholar 

  7. Ferrucci, F., Salza, P., Sarro, F.: Using hadoop mapreduce for parallel genetic algorithms: a comparison of the global, grid and island models. ECJ 26(4), 535–567 (2018)

    Google Scholar 

  8. Gorla, A., Tavecchia, I., Gross, F., Zeller, A.: Checking app behavior against app descriptions. In: ICSE 2014 (2014)

    Google Scholar 

  9. Harman, M., Al-Subaihin, A., Jia, Y., Martin, W., Sarro, F., Zhang, Y.: Mobile app and app store analysis, testing and optimisation. In: MOBILESoft 2016 (2016)

    Google Scholar 

  10. Hruschka, E., Campello, R., Freitas, A., de Carvalho, A.: A survey of evolutionary algorithms for clustering. IEEE TCMCC 39(2), 133–155 (2009)

    Google Scholar 

  11. Huang, J., Liu, J., Yao, X.: A multi-agent evolutionary algorithm for software module clustering problems. Soft Comput. 21(12), 3415–3428 (2016). https://doi.org/10.1007/s00500-015-2018-5

    Article  Google Scholar 

  12. Martin, W., Sarro, F., Jia, Y., Zhang, Y., Harman, M.: A survey of app store analysis for software engineering. IEEE TSE 43(9), 817–847 (2017)

    Google Scholar 

  13. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33(9), 1455–1465 (2000)

    Article  Google Scholar 

  14. Nayebi, M., Farrahi, H., Lee, A., Cho, H., Ruhe, G.: More insight from being more focused: analysis of clustered market apps. In: WAMA 2016 (2016)

    Google Scholar 

  15. Praditwong, K., Harman, M., Yao, X.: Software module clustering as a multi-objective search problem. IEEE TSE 37(2), 264–282 (2011)

    Google Scholar 

  16. Rodrigues, J., Vasconcelos, G., Tin’os, R.: GAMA: Genetic Approach to Maximize Clustering Criterion (2019). https://github.com/jairsonrodrigues/gama

  17. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. JCAM 20, 53–65 (1987)

    MATH  Google Scholar 

  18. Sarro, F., Harman, M., Jia, Y., Zhang, Y.: Customer rating reactions can be predicted purely using app features. In: RE 2018 (2018)

    Google Scholar 

  19. Sarro, F., Al-Subaihin, A.A., Harman, M., Jia, Y., Martin, W., Zhang, Y.: Feature lifecycles as they spread, migrate, remain, and die in app stores. In: RE 2015 (2015)

    Google Scholar 

  20. Sarro, F., Petrozziello, A., He, D.Q., Yoo, S.: A new approach to distribute MOEA pareto front computation. In: GECCO 2020 (2020)

    Google Scholar 

  21. Scrucca, L.: GA: a package for genetic algorithms in R. J. Stat. Softw. 53(4), 1–37 (2013)

    Article  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federica Sarro
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59761-0

  • Online ISBN: 978-3-030-59762-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation