An Enhanced Multifactor Multiobjective Approach for Software Modularization

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Khan, Muhammad Zakir
Naseem, Rashid
Haq, Ijaz Ul
Hussain, Saddam
Alroobaea, Roobaea
Ullah, Syed Sajid
Umar, Fazlullah
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cc-by (c) Muhammad Zakir Khan et al., 2022
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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).
Journal or Serie
Mathematical Problems in Engineering, 2022, vol. 2022, 7960610