A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Kaur, Inderpreet; Kaur, Amandeep

doi:10.1109/access.2021.3049823

Cited by 27 publications

(5 citation statements)

References 49 publications

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“…A few other authors [22,30,31,33,42] also worked on the same code smell datasets. These authors used machine learning and stack ensemble learning algorithms.…”

Section: Results Comparison Of Our Approach With Others' Correlated Workmentioning

confidence: 99%

Code Smell Detection Using Ensemble Machine Learning Algorithms

et al. 2022

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Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells. In this study, we applied five ensemble machine learning and two deep learning algorithms to detect code smells. Four code smell datasets were analyzed: the Data class, the God class, the Feature-envy, and the Long-method datasets. In previous works, machine learning and stacking ensemble learning algorithms were applied to this dataset and the results found were acceptable, but there is scope of improvement. A class balancing technique (SMOTE) was applied to handle the class imbalance problem in the datasets. The Chi-square feature extraction technique was applied to select the more relevant features in each dataset. All five algorithms obtained the highest accuracy—100% for the Long-method dataset with the different selected sets of metrics, and the poorest accuracy, 91.45%, was achieved by the Max voting method for the Feature-envy dataset for the selected twelve sets of metrics.

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“…A few other authors [22,30,31,33,42] also worked on the same code smell datasets. These authors used machine learning and stack ensemble learning algorithms.…”

Section: Results Comparison Of Our Approach With Others' Correlated Workmentioning

confidence: 99%

Code Smell Detection Using Ensemble Machine Learning Algorithms

et al. 2022

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“…Além disso, identificaram limitações e propuseram direções para pesquisas futuras, como a inclusão de mais tipos de code smells e a avaliação de técnicas de balanceamento de dados e aprendizado em conjunto. Kaur and Kaur [2021] apresentaram a técnica de aprendizado de conjunto (Ensemble Learning) e a técnica de seleção de características de correlação em três conjuntos de dados Java de código aberto para detecção de code smells. Eles aplicaram o classificador Bagging e Floresta Aleatória para analisar cada abordagem com quatro medidas de desempenho: acurácia (P1), G-mean 1 (P2), G-mean 2 (P3) e F-measure (P4).…”

Section: Trabalhos Relacionadosunclassified

Estudo empírico: detecção de Code Smells com aprendizado de máquinas

Moreira,

Braz,

Ferreira

et al. 2024

Anais Do XXVII Congresso Ibero-Americano Em Engenharia De Software (CIbSE 2024)

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A detecção de code smells durante o processo de desenvolvimento de software é importante para melhorar a qualidade do software e a refatoração é fundamental para eliminar esses indícios de problema. Este estudo avalia uma abordagem empírica que se baseia no treinamento de cinco algoritmos de aprendizado de máquina para detectar code smells em sistemas de software, utilizando métricas de software como parâmetros. Os resultados mostram que a abordagem de aprendizado de máquina tive um excelente desempenho para a detecção de code smells, alcançando uma acurácia entre 93,7% a 99,2%.

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“…The CS detection was proposed [9] using ML techniques like static code metrics and CS metrics in the dataset. Initially, two classification models like Bagging and Random Forest were used for enchaining the performance for CS detection.…”

Section: Literature Surveymentioning

confidence: 99%

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

2022

IJIES

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Code smells (CS) are a severe symptom in software source code that leads to a serious problem in software maintenance and evolution. Feature Envy (FE) is a type of CS that refers to methods that are misplaced in the source code. Many tools like infusion, JDeodorant and JspIRIT have been used to identify the CS. The conversion of source code measurements into predictions is based on manually constructed heuristics. But, manually selecting good features and constructing optimal heuristics is a challenging issue. So, automatic detection of CS is required. The Deep Learning (DL) methods play an important role in achieving the automatic detection of FE(DLFED). The FE detection approach based on DL is intended to discover misplaced methods. The DL technique automatically identifies the source code characteristics for FE identification as well as automatically improves the difficult mapping structure between the features and their prediction. But, a DL-based automatic detection of CS requires a significant number of features with labeled training datasets to provide better prediction results. Therefore, an extension of the DL-based FE detection technique (EDLFED-FM) is proposed in this article to detect both misplaced methods and fields. The semantic relationship between identifiers in the source code program is used to extract the features of misplaced fields and methods. The new decomposition slice method is proposed to convert the extracted features of misplaced methods and fields into the slicing vector composition. This sliced feature helps deep learners understand the relationship between misplaced methods and fields. This automated method is capable of producing labeled training data for DL techniques-based classifiers without the requirement for human intervention. Finally, the experimental outcomes proved that precision rate of suggested method outperforms the JDeodorant and DLFED methods by 14.49% and 10.29 % for free plane, 14.72 % and 12.2 % for Junit, and 21.84% and 14.68 % for JExcelapi applications. Hence, it has been demonstrated that the predicted misplaced methods and fields are moved to the refactoring class for positive testing of source code program with less training data.

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A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Cited by 27 publications

References 49 publications

Code Smell Detection Using Ensemble Machine Learning Algorithms

Code Smell Detection Using Ensemble Machine Learning Algorithms

Estudo empírico: detecção de Code Smells com aprendizado de máquinas

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

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