An Enhanced Multifactor Multiobjective Approach for Software Modularization. (8th June 2022)

• Record Type: Journal Article
• Authors: Zakir Khan, Muhammad; Naseem, Rashid; Anwar, Aamir; ul-Haq, Ijaz; Hussain, Saddam; Alroobaea, Roobaea; Ullah, Syed Sajid; Umar, Fazlullah
• Other Names: Ali Ghous Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-06-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/7960610
• Languages: English
• ISSNs: 1024-123X

Abstract

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).