Khan, M. Z., Naseem, R., Anwar, Aamir, Ul-Haq, I. and Umar, F. (2022) An Enhanced Multifactor Multiobjective Approach for Software Modularization. Mathematical Problems in Engineering.

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Abstract

Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

Item Type:	Article
Identifier:	10.1155/2022/7960610
Subjects:	Computing
Depositing User:	Marc Forster
Date Deposited:	11 Nov 2024 12:41
Last Modified:	11 Nov 2024 12:45
URI:	https://repository.uwl.ac.uk/id/eprint/12876

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