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A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

Archana Patnaik, Neelamdhab Padhy
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 15
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781799860617|DOI: 10.4018/IJOSSP.2021040102
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MLA

Patnaik, Archana, and Neelamdhab Padhy. "A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms." IJOSSP vol.12, no.2 2021: pp.21-35. http://doi.org/10.4018/IJOSSP.2021040102

APA

Patnaik, A. & Padhy, N. (2021). A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms. International Journal of Open Source Software and Processes (IJOSSP), 12(2), 21-35. http://doi.org/10.4018/IJOSSP.2021040102

Chicago

Patnaik, Archana, and Neelamdhab Padhy. "A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms," International Journal of Open Source Software and Processes (IJOSSP) 12, no.2: 21-35. http://doi.org/10.4018/IJOSSP.2021040102

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Abstract

Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.

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