research-article

Multi-dimensional information-driven many-objective software remodularization approach

Authors:

Amarjeet Prajapati,

Anshu Parashar,

Amit RatheeAuthors Info & Claims

Frontiers of Computer Science, Volume 17, Issue 3

https://doi.org/10.1007/s11704-022-1449-2

Published: 08 November 2022 Publication History

Abstract

Most of the search-based software remodularization (SBSR) approaches designed to address the software remodularization problem (SRP) areutilizing only structural information-based coupling and cohesion quality criteria. However, in practice apart from these quality criteria, there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software systems. Therefore, consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization solution. Additionally, such modularization can be good from the quality metrics perspective but may not be acceptable to the developers. To produce a remodularization solution acceptable from both quality metrics and developers’ perspectives, this paper exploited more dimensions of software information to define the quality criteria as modularization objectives. Further, these objectives are simultaneously optimized using a tailored many-objective artificial bee colony (MaABC) to produce a remodularization solution. To assess the effectiveness of the proposed approach, we applied it over five software projects. The obtained remodularization solutions are evaluated with the software quality metrics and developers view of remodularization. Results demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.

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Cited By

He YWu YQian CZhou Z(2024)Margin distribution and structural diversity guided ensemble pruningMachine Language10.1007/s10994-023-06429-3113:6(3545-3567)Online publication date: 18-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06429-3
Mann ASwift SArzoky M(2024)Applying Graph Partitioning-Based Seeding Strategies to Software ModularisationApplications of Evolutionary Computation10.1007/978-3-031-56852-7_16(240-258)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_16

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Information & Contributors

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Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 17, Issue 3

Jun 2023

238 pages

ISSN:2095-2228

EISSN:2095-2236

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© Higher Education Press 2023.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 November 2022

Accepted: 24 May 2022

Received: 16 August 2021

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Cited By

He YWu YQian CZhou Z(2024)Margin distribution and structural diversity guided ensemble pruningMachine Language10.1007/s10994-023-06429-3113:6(3545-3567)Online publication date: 18-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06429-3
Mann ASwift SArzoky M(2024)Applying Graph Partitioning-Based Seeding Strategies to Software ModularisationApplications of Evolutionary Computation10.1007/978-3-031-56852-7_16(240-258)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_16

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