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Multi-objective Variable Neighborhood Search for Improving Software Modularity

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Variable Neighborhood Search (ICVNS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13863))

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Abstract

Search-Based Software Engineering is a research area that aims to tackle software engineering tasks as optimization problems. Among the problems in this area, we can find the Software Module Clustering Problem (SMCP). This problem, which has been proved to be \(\mathcal{N}\mathcal{P}\)-hard, focuses on finding the best organization of a software project in terms of modularity. Since modular code is easier to understand, the result is an increase of the quality of software projects and thus a reduction of the costs associated to their maintenance. To tackle the SMCP, software projects are often modeled as graphs that represent the dependencies between different components. In this work, we study two well-known multi-objective approaches for the SMCP: the Maximizing Cluster Approach (MCA) and the Equal-size Cluster Approach (ECA). Each of these variants is composed of 5 different objectives. We propose a heuristic algorithm based on the Multi-Objective Variable Neighborhood Descent (MO-VND) schema to tackle the aforementioned variants and we introduce three neighborhoods to be explored within the algorithm. Finally, we compare the performance of our proposal with the performance of NSGA-III over a dataset of real software projects. The results show that the proposed algorithm is competitive when tackling the MCA, and some ideas are given to increase its efficiency when tackling the ECA.

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Acknowledgements

This research has been partially supported by grants: PID2021-125709OA-C22 and PID2021-126605NB-I00, funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’; grant P2018/TCS-4566, funded by Comunidad de Madrid and European Regional Development Fund; grant CIAICO/2021/224 funded by Generalitat Valenciana; and grant M2988 funded by Proyectos Impulso de la Universidad Rey Juan Carlos 2022.

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Authors and Affiliations

  1. Universidad Rey Juan Carlos, Móstoles, 28933, Madrid, Spain

    Javier Yuste, Eduardo G. Pardo & Abraham Duarte

Authors
  1. Javier Yuste
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  2. Eduardo G. Pardo
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  3. Abraham Duarte
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Correspondence to Javier Yuste .

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Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Andrei Sleptchenko

  2. University of Macedonia, Thessaloniki, Greece

    Angelo Sifaleras

  3. HEC Montréal, Montréal, QC, Canada

    Pierre Hansen

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Yuste, J., Pardo, E.G., Duarte, A. (2023). Multi-objective Variable Neighborhood Search for Improving Software Modularity. In: Sleptchenko, A., Sifaleras, A., Hansen, P. (eds) Variable Neighborhood Search. ICVNS 2022. Lecture Notes in Computer Science, vol 13863. Springer, Cham. https://doi.org/10.1007/978-3-031-34500-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-34500-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34499-2

  • Online ISBN: 978-3-031-34500-5

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