The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
DeleSmell: Code smell detection based on deep learning and latent semantic analysis
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In the citing paper
“It has been evidenced that augmenting the model size (scaling) serves as a dependable strategy for enhancing generalization, thereby facilitating additional functionalities, without encountering performance plateaus (Kaplan et al. 2020; Zhang et al. 2022; Chowdhery et al. 2022; Hoffmann et al. 2022; Wei et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Zhang et al. 2022
In the citing paper
“Furthermore, LSA can be utilized to approximate human judgment, aiding in the anticipation of semantic connections among different textual segments, and providing computational estimations of semantic similarities between words (Singh et al. 2022; Zhang et al. 2022; Al-Hashedi et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Zhang et al. 2022
In the citing paper
“The utilization of LSA substantially enhances our capacity to extract and discern the semantic associations that manifest in the thoughts of speakers or writers during communication.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Zhang et al. 2022
Citations
Cites
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
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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
Research works printed but not linked: 3
- E.V. Emden, L. Moonen, Java quality assurance by detecting code smells, in: Working Conference on Reverse Engineering, 2002, pp. 97–106.
- C. Marinescu, R. Marinescu, P.F. Mihancea, D. Ratiu, R. Wettel, iPlasma: An Integrated Platform for Quality Assessment of Object-Oriented Design, in: IEEE International Conference on Software Maintenance-Industrial and Tool Volume, 2005, pp. 77–80.
- Sai, 2017, Detection of large class based on latent semantic analysis, Comput. Sci., 44, 495
Cited by
Sorted by citations
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
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
Metadata
- Authors
- Zhang, Y., Ge, C., Hong, S., Tian, R., Dong, C., & Liu, J.
- Affiliations
- Hebei University of Science and Technology (CN)
- China United Network Communications Group (China) (CN)
- Beijing Institute of Technology (CN)
- Corresponding author
- Zhang, Yang
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 255
- Pages
- 109737
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4292622331
- Cited by
- 54
- References
- 39
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 11 · 2023: 14
- FWCI
- 10.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Software › Model-Driven Software Engineering Techniques
- Keywords
- code smell detection, latent semantic analysis, Word2Vec, structural features, semantic features, class imbalance, refactoring tools, convolutional neural networks, support vector machine, software maintainability
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- CoChat. (2026). Citation check: DeleSmell: Code smell detection based on deep learning and latent semantic analysis. https://app.cochat.ai/papers/10.1016/j.knosys.2022.109737
- The paper
- Zhang, Y., Ge, C., Hong, S., Tian, R., Dong, C., & Liu, J. (2022). DeleSmell: Code smell detection based on deep learning and latent semantic analysis. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2022.109737
