FedCSD: A Federated Learning Based Approach for Code-Smell Detection
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 an...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10477413/ |
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| author | Sadi Alawadi Khalid Alkharabsheh Fahed Alkhabbas Victor R. Kebande Feras M. Awaysheh Fabio Palomba Mohammed Awad |
| author_facet | Sadi Alawadi Khalid Alkharabsheh Fahed Alkhabbas Victor R. Kebande Feras M. Awaysheh Fabio Palomba Mohammed Awad |
| author_sort | Sadi Alawadi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T15:40:24Z |
| format | Article |
| id | doaj.art-25e1d0a0fe8546fbb329d4970f0edef5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T15:40:24Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25e1d0a0fe8546fbb329d4970f0edef52024-04-01T23:00:32ZengIEEEIEEE Access2169-35362024-01-0112448884490410.1109/ACCESS.2024.338016710477413FedCSD: A Federated Learning Based Approach for Code-Smell DetectionSadi Alawadi0https://orcid.org/0000-0002-6309-2892Khalid Alkharabsheh1https://orcid.org/0000-0002-3182-418XFahed Alkhabbas2https://orcid.org/0000-0002-8025-4734Victor R. Kebande3https://orcid.org/0000-0003-4071-4596Feras M. Awaysheh4Fabio Palomba5https://orcid.org/0000-0001-9337-5116Mohammed Awad6https://orcid.org/0000-0002-5053-0785Department of Computer Science, Blekinge Institute of Technology, Karlskrona, SwedenSoftware Engineering Department, Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, As-Salt, JordanInternet of Things and People Research Center, Malmö University, Malmö, SwedenDepartment of Computer Science, Blekinge Institute of Technology, Karlskrona, SwedenInstitute of Computer Science, Delta Research Centre, University of Tartu, Tartu, EstoniaDepartment of Computer Science, University of Salerno, Fisciano, ItalyDepartment of Computer Systems Engineering, Arab American University, Jenin, PalestineSoftware 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.https://ieeexplore.ieee.org/document/10477413/Software qualitytechnical debitfederated learningprivacy-preservingcode smell detection |
| spellingShingle | Sadi Alawadi Khalid Alkharabsheh Fahed Alkhabbas Victor R. Kebande Feras M. Awaysheh Fabio Palomba Mohammed Awad FedCSD: A Federated Learning Based Approach for Code-Smell Detection IEEE Access Software quality technical debit federated learning privacy-preserving code smell detection |
| title | FedCSD: A Federated Learning Based Approach for Code-Smell Detection |
| title_full | FedCSD: A Federated Learning Based Approach for Code-Smell Detection |
| title_fullStr | FedCSD: A Federated Learning Based Approach for Code-Smell Detection |
| title_full_unstemmed | FedCSD: A Federated Learning Based Approach for Code-Smell Detection |
| title_short | FedCSD: A Federated Learning Based Approach for Code-Smell Detection |
| title_sort | fedcsd a federated learning based approach for code smell detection |
| topic | Software quality technical debit federated learning privacy-preserving code smell detection |
| url | https://ieeexplore.ieee.org/document/10477413/ |
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