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- Alkharabsheh, Khalid, et al.
(författare)
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A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset : A study of God class
- 2022
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Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 2. |
- Alkharabsheh, Khalid, et al.
(författare)
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Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 145191-145211
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Tidskriftsartikel (refereegranskat)abstract
- The automatic detection of Design Smells has evolved in parallel to the evolution of automatic refactoring tools. There was a huge rise in research activity regarding Design Smell detection from 2010 to the present. However, it should be noted that the adoption of Design Smell detection in real software development practice is not comparable to the adoption of automatic refactoring tools. On the basis of the assumption that it is the objectiveness of a refactoring operation as opposed to the subjectivity in definition and identification of Design Smells that makes the difference, in this paper, the lack of agreement between different evaluators when detecting Design Smells is empirically studied. To do so, a series of experiments and studies were designed and conducted to analyse the concordance in Design Smell detection of different persons and tools, including a comparison between them. This work focuses on two well known Design Smells: God Class and Feature Envy. Concordance analysis is based on the Kappa statistic for inter-rater agreement (particularly Kappa-Fleiss). The results obtained show that there is no agreement in detection in general, and, in those cases where a certain agreement appears, it is considered to be a fair or poor degree of agreement, according to a Kappa-Fleiss interpretation scale. This seems to confirm that there is a subjective component which makes the raters evaluate the presence of Design Smells differently. The study also raises the question of a lack of training and experience regarding Design Smells.
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| 3. |
- Alkharabsheh, Khalid, et al.
(författare)
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Prioritization of god class design smell : A multi-criteria based approach
- 2022
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Ingår i: JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES. - Amsterdam : Elsevier. - 1319-1578 .- 2213-1248. ; 34:10, s. 9332-9342
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Tidskriftsartikel (refereegranskat)abstract
- Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle.Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell.Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset.Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done.
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