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  • 1.
    Alawadi, Sadi
    et al.
    Blekinge Inst Technol, Dept Comp Sci, S-37179 Karlskrona, Sweden.;Univ Santiago de Compostela, Comp Graph & Data Engn COGRADE Res Grp, Santiago De Compostela 15705, Spain..
    Alkharabsheh, Khalid
    Al Balqa Appl Univ, Prince Abdullah bin Ghazi Fac Informat & Commun Te, Software Engn Dept, As Salt 19117, Jordan..
    Alkhabbas, Fahed
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Kebande, Victor R.
    Blekinge Inst Technol, Dept Comp Sci, S-37179 Karlskrona, Sweden..
    Awaysheh, Feras M.
    Univ Tartu, Inst Comp Sci, Delta Res Ctr, EE-51009 Tartu, Estonia..
    Palomba, Fabio
    Univ Salerno, Dept Comp Sci, I-84084 Fisciano, Italy..
    Awad, Mohammed
    Arab Amer Univ, Dept Comp Syst Engn, Jenin 00970, Palestine..
    FedCSD: A Federated Learning Based Approach for Code-Smell Detection2024In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 44888-44904Article in journal (Refereed)
    Abstract [en]

    Software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model's highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers.

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