FedCSD: A Federated Learning Based Approach for Code-Smell Detection

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Alawadi, Sadi,1983-Blekinge Tekniska Högskola,Institutionen för datavetenskap (author)

FedCSD : A Federated Learning Based Approach for Code-Smell Detection

Article/chapterEnglish2024

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Institute of Electrical and Electronics Engineers (IEEE),2024
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LIBRIS-ID:oai:DiVA.org:bth-26101
https://urn.kb.se/resolve?urn=urn:nbn:se:bth-26101URI
https://doi.org/10.1109/ACCESS.2024.3380167DOI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

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. © 2013 IEEE.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Programvaruteknik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Software Engineering hsv//eng
code smell detection
federated learning
privacy-preserving
Software quality
technical debit
Application programs
Codes (symbols)
Computer software maintenance
Computer software selection and evaluation
Cryptography
Object oriented programming
Odors
Code
Code smell
Ho-momorphic encryptions
Homomorphic-encryptions
Object oriented modelling
Privacy preserving
Data privacy

Added entries (persons, corporate bodies, meetings, titles ...)

Alkharabsheh, KhalidAl-Balqa Applied University, Jordan (author)
Alkhabbas, FahedMalmö University, Internet of Things and People Research Center (author)
Kebande, Victor R.Blekinge Tekniska Högskola,Institutionen för datavetenskap(Swepub:bth)vvk (author)
Awaysheh, Feras M.Institute of Computer Science, Estonia (author)
Palomba, FabioUniversity of Salerno, Italy (author)
Awad, MohammedArab American University, Palestine (author)
Blekinge Tekniska HögskolaInstitutionen för datavetenskap (creator_code:org_t)

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In:IEEE Access: Institute of Electrical and Electronics Engineers (IEEE)12, s. 44888-449042169-3536

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Software Enginee ...

Articles in the publication: IEEE Access

By the university: Blekinge Institute of Technology

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