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

Zhang, Yang; Ge, Chuyan; Hong, Su Young; Tian, Ruili; Dong, Chunhao; Liu, Jingjing

doi:10.1016/j.knosys.2022.109737

Cited by 26 publications

(10 citation statements)

References 26 publications

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“…Their results presented that the accuracy and validity of these two methods for detecting code smell still need further investigation. Some studies Sharma et al (2021); Yu et al (2021); Zhang and Dong (2021); Li and Zhang;Zhang et al (2022) tried to apply deep learning techniques for CSD, and their conclusions examined that some deep learning models accept a better performance on CSD, i.e., Convolutional Neural Networks (CNN-1) Sharma et al (2021); Zhang et al (2022), recurrent neural network Sharma et al (2021), long short-term memory Yu et al (2021); Li and Zhang, residual network Zhang and Dong (2021), and attention Zhang et al (2022).…”

Section: Literature Reviewmentioning

confidence: 99%

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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Machine learning-based code smell detection has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for code smell detection, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques on machine learning-based code smell detection, we examine 31 imbalanced learning techniques with seven classifiers to build code smell detection models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed-rank test and Cliff’s δ. The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over-sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best-performing imbalanced learning techniques and the top-3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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Section: Literature Reviewmentioning

confidence: 99%

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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“…Zhang et al [34] have suggested an approach DeleSmell to identify code smells using a deep learning model. A refactoring tool has been developed to convert a normal method into a brain method (BM) and a normal class into a brain class (BC).…”

Section: Related Workmentioning

confidence: 99%

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

Thessalonica

Nehemiah

Sreejith

et al. 2023

Scientific Programming

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Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.

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“…However, some methods may result in the deletion of important keywords or the ignoring of critical vulnerability features during data processing 22 . Additionally, some of the models used may have an insufficient understanding of the semantic characteristics of vulnerability code programs 23 , which can result in false negatives. To address these issues, we utilized the CodeBERT pre-training model for data preprocessing.…”

Section: Deep Learning For Smart Contract Vulnerabilities Detectionmentioning

confidence: 99%

Lightning Cat: A Deep Learning-based Solution for Smart Contracts Vulnerability Detection

Tang

Lai

et al. 2023

Preprint

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This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process.In this paper, we introduce a solution called Lighting Cat which is based on deep learning techniques. We trained three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. To precisely extract vulnerability features, we acquired segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately, thereby enhancing the performance of vulnerabilities detection. This is particularly significant in the inspection of Solidity Code.To demonstrate the feasibility of our proposed solution, we evaluated its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types. Experimental results showed that, among the Lighting Cat we proposed, Optimized-CodeBERT model surpassed other methods, achieving an f1-score of 93.53%.

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

Cited by 26 publications

References 26 publications

On the Relative Value of Imbalanced Learning for Code Smell Detection

On the Relative Value of Imbalanced Learning for Code Smell Detection

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

Lightning Cat: A Deep Learning-based Solution for Smart Contracts Vulnerability Detection

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