DeleSmell: Code smell detection based on deep learning and latent semantic analysis

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Data preparation for Deep Learning based Code Smell Detection: A systematic literature review

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Cross-Level Requirements Tracing Based on Large Language Models

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EnseSmells : Deep ensemble and programming language models for automated code smells detection

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Code Smell Detection Research Based on Pre-training and Stacking Models

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Application of Deep Learning for Code Smell Detection: Challenges and Opportunities

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CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction

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DeepLog: Deep-Learning-Based Log Recommendation

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MIRROR: multi-objective refactoring recommendation via correlation analysis

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Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

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Graph neural network-based long method and blob code smell detection

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COSTAR: Software Code Smell Detection Through Tree-Based Abstract Representation

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

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

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On the Relative Value of Imbalanced Learning for Code Smell DetectionPreprint

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Data Preparation for Deep Learning Based Code Smell Detection: A Systematic Literature ReviewPreprint

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SyntaPulse: An unsupervised framework for sentiment annotation and semantic topic extraction

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Attentionsmelling: Using Large Language Models to Identify Code Smells

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Systematic literature review on software code smell detection approaches

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DIR-SMOTE: a density-influence resampling framework for imbalanced code smell detection

Malhotra, R., Jain, B., & Kessentini, M. (2026) · Automated Software Engineering · doi:10.1007/s10515-026-00624-x · Cited by 2

Enhancing software code smell detection with modified cost-sensitive SVM

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A Semisupervised Learning Approach for Code Smell Detection

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Machine Learning Powered Code Smell Detection as a Business Improvement Tool

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ReInstancer: An automatic refactoring approach for Instanceof pattern matching

Zhang, Y., & Hong, S. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2661 · Cited by 1

Predicting a Program’s Execution Time After Move Method Refactoring Based on Deep Learning and Feature Interaction

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QNet: exploring deep learning for quantum code smell detection

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Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach

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Refactoring Detection Across Languages: Leveraging Java-Trained Models for Detecting Class-Level Refactorings in Kotlin

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Performance Prediction for Lock-based Programs

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An Empirical Evaluation of Design Smells and Code Smells Over Multiple Versions of Software Evolution

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Predicting Software Reliability Through Machine Learning Analysis of Code Smells

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

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An Approach Based on Sum Product Networks for Code Smells Detection

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Exploring Topic Modelling Techniques on Indian News: a Comparitive Study

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Repository-Level Code Smell Detection Based on Multi-Scale Code Information and LLM Assistance

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Code Smell Detection Framework Using Self Attention CNN: An Explainable Feature Selection Approach

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Evaluating Human- and AI-Generated Code Quality Based on Code Smell

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Unearthing Code Smells: An In‐Depth Exploration of Machine Learning Techniques in Code Smell Detection

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A survey on code smells detection using machine learning techniques

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QUAMET: Structural and Semantic Metric Analysis for Quantum Code Smell DetectionPreprint

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An Overlooked Baseline Artifact in Comparing Specialized and Pooled Classifiers

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Deep Learning Based Code Smell Detection

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Code Smell Detection Research Based on Pre-training and Stacking Models

Zhang, D., Song, S., Zhang, Y., Liu, H., & Shen, G. (2023) · IEEE Latin America Transactions · doi:10.1109/tla.2024.10375735 · Cited by 9

Feature Envy Detection with Deep Learning and Snapshot Ensemble

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