A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 27, 2023

Abstract Detecting code smells may be highly helpful for reducing maintenance costs and raising source quality. Code facilitate developers or researchers to understand several types of design flaws. with high severity can cause significant problems the software challenges system's maintainability. It is quite essential assess detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances difficulties smell detection. In this study, four datasets (Data class, God Feature envy, Long method) are selected detect severity. work, an effort made address issue imbalance, which, Synthetic Minority Oversampling Technique (SMOTE) balancing technique applied. Each dataset's relevant features chosen using a feature selection based on principal component analysis. determined five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, Logistic Regression. This study obtained 0.99 accuracy score forest tree approach method smell. model's performance compared its three other measurements (Precision, Recall, F-measure) estimate classification models. impact presented without applying SMOTE. results promising beneficial paving way studies area.

Language: Английский

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Severity Classification of Code Smells Using Machine-Learning Methods

SN Computer Science, Journal Year: 2023, Volume and Issue: 4(5)

Published: July 29, 2023

Language: Английский

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

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 44888 - 44904

Published: Jan. 1, 2024

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

Language: Английский

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Ensemble methods with feature selection and data balancing for improved code smells classification performance

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109527 - 109527

Published: Oct. 28, 2024

Language: Английский

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Using word embedding and convolution neural network for bug triaging by considering design flaws

Science of Computer Programming, Journal Year: 2023, Volume and Issue: 228, P. 102945 - 102945

Published: March 27, 2023

Language: Английский

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Alleviating class imbalance in Feature Envy prediction: An oversampling technique based on code entity attributes

Information and Software Technology, Journal Year: 2025, Volume and Issue: 180, P. 107673 - 107673

Published: Jan. 15, 2025

Language: Английский

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Code Smell Detection Using Deep Learning Models to Enhance the Software Quality

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 31 - 43

Published: Jan. 1, 2025

Language: Английский

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A Semisupervised Learning Approach for Code Smell Detection

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Feb. 6, 2025

Language: Английский

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An Empirical Study on Feature Extraction Using Ensemble Deep Learning for Code Smell Detection

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 573 - 585

Published: Jan. 1, 2025

Language: Английский

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Data preprocessing for machine learning based code smell detection: A systematic literature review

Information and Software Technology, Journal Year: 2025, Volume and Issue: unknown, P. 107752 - 107752

Published: April 1, 2025

Language: Английский

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