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Performance of Genetic Algorithms in the Context of Software Model Refactoring

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Computer Performance Engineering and Stochastic Modelling (EPEW 2023, ASMTA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14231))

Abstract

Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., \(\texttt{PESA2}\) seems to be the fastest one, while \(\texttt{NSGAII}\) shows the least memory usage).

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Notes

  1. 1.

    Replication package: https://github.com/danieledipompeo/replication-package__Perf-Comp-GA-4-Multi-Obj-SW-Model-Ref.

  2. 2.

    https://github.com/SEALABQualityGroup/2022-ist-replication-package/tree/main/case-studies/train-ticket.

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Acknowledgments

Daniele Di Pompeo and Michele Tucci are supported by European Union - NextGenerationEU - National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) - Project: “SoBigData.it - Strengthening the Italian RI for Social Mining and Big Data Analytics” - Prot. IR0000013 - Avviso n. 3264 del 28/12/2021.

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Authors and Affiliations

  1. University of L’Aquila, L’Aquila, Italy

    Vittorio Cortellessa, Daniele Di Pompeo & Michele Tucci

Authors
  1. Vittorio Cortellessa
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  2. Daniele Di Pompeo
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  3. Michele Tucci
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Correspondence to Daniele Di Pompeo .

Editor information

Editors and Affiliations

  1. Università degli Studi della Campania, Caserta, Italy

    Mauro Iacono

  2. Università degli Studi di Messina, Messina, Italy

    Marco Scarpa

  3. Università Cattolica del Sacro Cuore, Brescia, Italy

    Enrico Barbierato

  4. Università degli Studi di Messina, Messina, Italy

    Salvatore Serrano

  5. Università del Piemonte Orientale, Alessandria, Italy

    Davide Cerotti

  6. Universita degli Studi di Messina, Messina, Italy

    Francesco Longo

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Cortellessa, V., Di Pompeo, D., Tucci, M. (2023). Performance of Genetic Algorithms in the Context of Software Model Refactoring. In: Iacono, M., Scarpa, M., Barbierato, E., Serrano, S., Cerotti, D., Longo, F. (eds) Computer Performance Engineering and Stochastic Modelling. EPEW ASMTA 2023 2023. Lecture Notes in Computer Science, vol 14231. Springer, Cham. https://doi.org/10.1007/978-3-031-43185-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-43185-2_16

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