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A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset : A study of God class
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- Alkharabsheh, Khalid (författare)
- Al-Balqa Applied University, JOR,Al Balqa Appl Univ BAU, Prince Abdullah Bin Ghazi Fac Informat & Commun T, Dept Software Engn, Salt, Jordan.
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- Alawadi, Sadi, 1983- (författare)
- Uppsala universitet,Avdelningen för beräkningsvetenskap,Uppsala University, SWE
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- Kebande, Victor R., 1985- (författare)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap,Blekinge Inst Technol, Dept Comp Sci DIDA, S-37179 Karlskrona, Sweden.
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- Crespo, Yania (författare)
- Universidad de Valladolid, ESP,Univ Valladolid, Dept Informat, Escuela Ingn Informat, Campus Miguel Delibes, Paseo Belen 15, Valladolid 47011, Spain.
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- Fernández-Delgado, Manuel (författare)
- Universidad de Santiago de Compostela, ESP,Univ Santiago de Compostela, Ctr Singular Invest Tecnoloxias Intelixentes, CiTIUS, Santiago De Compostela 15782, Spain.
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- Taboada, José A. (författare)
- Universidad de Santiago de Compostela, ESP,Univ Santiago de Compostela, Ctr Singular Invest Tecnoloxias Intelixentes, CiTIUS, Santiago De Compostela 15782, Spain.
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Al-Balqa Applied University, JOR Al Balqa Appl Univ BAU, Prince Abdullah Bin Ghazi Fac Informat & Commun T, Dept Software Engn, Salt, Jordan (creator_code:org_t)
- Elsevier B.V. 2022
- 2022
- Engelska.
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Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025. ; 143
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Abstract
Ämnesord
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- Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, such as Kappa, ROC, and MCC, have been used in the assessment of the classifier behavior. © 2021 The Authors
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Nyckelord
- Balanced data
- Design smell detection
- God class
- Machine learning
- Software quality
- Balancing
- Computer software selection and evaluation
- Learning algorithms
- Life cycle
- Odors
- Software design
- Balanced datasets
- Context design
- Imbalanced dataset
- Machine learning algorithms
- Machine learning approaches
- Performance
- Software-systems
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