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An Enhanced Multifactor Multiobjective Approach for Software Modularization

Author

Listed:
  • Muhammad Zakir Khan
  • Rashid Naseem
  • Aamir Anwar
  • Ijaz ul-Haq
  • Saddam Hussain
  • Roobaea Alroobaea
  • Syed Sajid Ullah
  • Fazlullah Umar
  • Ghous Ali

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).

Suggested Citation

  • Muhammad Zakir Khan & Rashid Naseem & Aamir Anwar & Ijaz ul-Haq & Saddam Hussain & Roobaea Alroobaea & Syed Sajid Ullah & Fazlullah Umar & Ghous Ali, 2022. "An Enhanced Multifactor Multiobjective Approach for Software Modularization," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, June.
    • Handle: RePEc:hin:jnlmpe:7960610
    DOI: 10.1155/2022/7960610
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