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A Hybrid Genetic Algorithm for Software Architecture Re-Modularization

Author

Listed:
  • Lifeng Mu

    (Shanghai University)

  • Vijayan Sugumaran

    (Oakland University
    Oakland University)

  • Fangyuan Wang

    (Shanghai University)

Abstract

Software architectures have become highly heterogeneous and difficult to maintain due to software evolution and continuous change. Therefore, a software system usually must be restructured in terms of modules containing relatively dependent components to address the system complexity. However, it is challenging to re-modularize systems automatically to improve their maintainability. In this paper, we present a new mathematical programming model for the software re-modularization problem. In contrast to previous research, a novel metric based on the principle of complexity balance is introduced to address the issue of over-cohesiveness. In addition, a hybrid genetic algorithm (HGA) is designed to automatically determine high-quality re-modularization solutions. In the proposed HGA, a heuristic based on edge contraction and vectorization techniques is designed first to generate feature-rich solutions and subsequently implant these solutions as seeds into the initial population. Finally, a customized genetic algorithm (GA) is employed to improve the solution quality. Two sets of test problems are employed to evaluate the performance of the HGA. The first set includes sixteen real-world instances and the second set contains 900 large-scale simulated data. The proposed HGA is compared with two widely adopted algorithms, i.e., the multi-start hill-climbing algorithm (HCA) and the genetic algorithms with group number encoding (GNE). Experimental and statistical results demonstrate that in most cases, the HGA can guarantee better quality solutions than HCA and GNE.

Suggested Citation

  • Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 2020. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 22(5), pages 1133-1161, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-019-09906-0
    DOI: 10.1007/s10796-019-09906-0
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    References listed on IDEAS

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