A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

View Sample PDF

Author(s): Archana Patnaik (Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur, India)and Neelamdhab Padhy (Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur, India)
Copyright: 2021
Volume: 12
Issue: 2
Pages: 15
Source title: International Journal of Open Source Software and Processes (IJOSSP)
Editor(s)-in-Chief: Marta Catillo (Università degli Studi del Sannio, Italy)
DOI: 10.4018/IJOSSP.2021040102

Keywords: Computer Science & IT / Information Science Reference / Open Source Software / Systems and Software Engineering

Purchase

View A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

Purchase

Abstract

Related Content

IRMA Sponsors