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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

Untitled work

(2006) · Springer eBooks · Cited by 8,123

Untitled work

(2012) · Cited by 8,009

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759

A survey of decision tree classifier methodology

Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/239 · Cited by 2,615

An empirical study of the naive Bayes classifier

Rish, I. (2001) · Cited by 2,532

Refactoring improving the design of existing code

Saca, M. A. (2017) · IEEE Central America and Panama Convention (CONCAPAN) · doi:10.1109/concapan.2017.8278488 · Cited by 2,077

Mapping authors in intellectual space: A technical overview

McCain, K. W. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6 · Cited by 987

Improving support vector machine classifiers by modifying kernel functions

Амари, Ш.-И., & Wu, S. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00032-5 · Cited by 952

Use of K-Nearest Neighbor classifier for intrusion detection

Liao, Y., & Vemuri, V. R. (2002) · Computers & Security · doi:10.1016/s0167-4048(02)00514-x · Cited by 809

DECOR: A Method for the Specification and Detection of Code and Design Smells

Moha, N., Guéhéneuc, Y., Duchien, L., & Meur, A.-F. L. (2009) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2009.50 · Cited by 803

A survey on software smells

Sharma, T., & Spinellis, D. D. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.12.034 · Cited by 270

Code smell severity classification using machine learning techniques

Fontana, F. A., & Zanoni, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.04.014 · Cited by 181

BDTEX: A GQM-based Bayesian approach for the detection of antipatterns

Khomh, F., Vaucher, S., Guéhéneuc, Y., & Sahraoui, H. A. (2010) · Journal of Systems and Software · doi:10.1016/j.jss.2010.11.921 · Cited by 175

Code smell detection by deep direct-learning and transfer-learning

Sharma, T., Efstathiou, V., Λουρίδας, Π., & Spinellis, D. D. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110936 · Cited by 167

Object-Oriented Metrics in Practice: Using Software Metrics to Characterize, Evaluate, and Improve the Design of Object-Oriented Systems

Mann, C. J. H. (2007) · Kybernetes · doi:10.1108/k.2007.06736eae.001 · Cited by 164

Deep Learning Based Code Smell Detection

Liu, H., Jin, J., Xu, Z., Bu, Y., Zou, Y., & Zhang, L. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2936376 · Cited by 141

A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection

Kessentini, W., Kessentini, M., Sahraoui, H. A., Bechikh, S., & Ouni, A. (2014) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2331057 · Cited by 132

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba, F., Tamburri, D. A., Fontana, F. A., Oliveto, R., Zaidman, A., & Serebrenik, A. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2883603 · Cited by 127

Code smell detection using feature selection and stacking ensemble: An empirical investigation

Alazba, A., & Aljamaan, H. (2021) · Information and Software Technology · doi:10.1016/j.infsof.2021.106648 · Cited by 93

Code smell detection using multi-label classification approach

Guggulothu, T., & Moiz, S. A. (2020) · Software Quality Journal · doi:10.1007/s11219-020-09498-y · Cited by 92

Experience report: Evaluating the effectiveness of decision trees for detecting code smells

Amorim, L., Costa, E. D. B., Antunes, N., Fonseca, B., & Ribeiro, M. (2015) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre.2015.7381819 · Cited by 85

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Palomba, F., Panichella, A., Zaidman, A., Oliveto, R., & Lucia, A. D. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2752171 · Cited by 69

On the role of data balancing for machine learning-based code smell detection

Pecorelli, F., Nucci, D. D., Roover, C. D., & Lucia, A. D. (2019) · doi:10.1145/3340482.3342744 · Cited by 59

A Support Vector Machine Based Approach for Code Smell Detection

Kaur, A., Jain, S., & Goel, S. (2017) · doi:10.1109/mlds.2017.8 · Cited by 44

Components of Random Forests

Łuczak, T., & Pittel, B. G. (1992) · Combinatorics Probability Computing · doi:10.1017/s0963548300000067 · Cited by 43

Detecting Code Smells using Deep Learning

Das, A. K., Yadav, S., & Dhal, S. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929628 · Cited by 36

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang, Y., & Dong, C. (2021) · Journal of Software Evolution and Process · doi:10.1002/smr.2403 · Cited by 26

Deep semantic-Based Feature Envy Identification

Guo, X., Shi, C., & Jiang, H. (2019) · Asia-Pacific Symposium on Internetware · doi:10.1145/3361242.3361257 · Cited by 24

On the adequacy of static analysis warnings with respect to code smell prediction

Pecorelli, F., Lujan, S., Lenarduzzi, V., Palomba, F., & Lucia, A. D. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10126-5 · Cited by 19

HBSniff: A static analysis tool for Java Hibernate object-relational mapping code smell detection

Huang, Z., Shao, Z., Fan, G., Yu, H., Yang, K., & Zhou, Z. (2022) · Science of Computer Programming · doi:10.1016/j.scico.2022.102778 · Cited by 16

How to improve deep learning for software analytics

Yedida, R., & Menzies, T. (2022) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3524842.3528458 · Cited by 16

Malicious Domain Name Detection Model Based on CNN-GRU-Attention

Jiang, Y., Jia, M., Zhang, B., & Deng, L. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9602373 · Cited by 12

Pseudo 2D Hidden Markov Models for Face Recognition Using Neural Network Coefficients

Bevilacqua, V., Daleno, D., Cariello, L., & Mastronardi, G. (2007) · IEEE Workshop on Automatic Identification Advanced Technologies (AutoID) · doi:10.1109/autoid.2007.380602 · Cited by 11

Inductive probabilistic taxonomy learning using singular value decomposition

Fallucchi, F., & Zanzotto, F. M. (2011) · Natural Language Engineering · doi:10.1017/s1351324910000197 · Cited by 10

Large language models (LLMs): survey, technical frameworks, and future challenges

Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395

Deep learning-based solution for smart contract vulnerabilities detection

Tang, X., Du, Y., Lai, A., Zhang, Z., & Shi, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-47219-0 · Cited by 76

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

Rao, R. S., Dewangan, S., Mishra, A., & Gupta, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-43380-8 · Cited by 45

A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms

Nasrabadi, M. Z., Parsa, S. A., Esmaili, E., & Palomba, F. (2023) · ACM Computing Surveys · doi:10.1145/3596908 · Cited by 45

Improving accuracy of code smells detection using machine learning with data balancing techniques

Khleel, N. A. A., & Nehéz, K. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06265-9 · Cited by 27

Deep Learning Based Code Smell Detection

Liu, H., Jin, J., Xu, Z., Bu, Y., Zou, Y., & Zhang, L. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2936376 · Cited by 141

Detecting Code Smells using Deep Learning

Das, A. K., Yadav, S., & Dhal, S. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929628 · Cited by 36

Code Smells Detection System Based on Machine Learning and Deep Learning TechniquesPreprint

Menshawy, R. S., Yousef, A. H., & Salem, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4149540

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

Zhang, M., & Jia, J. (2022) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa56465.2022.00037 · Cited by 12