Towards Predicting Software Defects with Clustering Techniques

Almayyan, Waheeda

doi:10.5121/ijaia.2021.12103

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Results confrmed the signifcant performance of the proposed model. Almayyan [58] evaluated diferent feature selection algorithms such as GWO, CS, Bat, and PSO for the prediction of software defects. Te NASA dataset benchmarks were analyzed using three clustering algorithms such as X-Means, Farthest First, and Self Organizing Map (SOM).…”

Section: Applications Of Gwo Woa Hho and Mfo In Softwarementioning

confidence: 99%

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

et al. 2023

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Swarm intelligence algorithms are metaheuristics inspired by the collective behavior of species such as birds, fish, bees, and ants. They are used in many optimization problems due to their simplicity, flexibility, and scalability. These algorithms get the desired convergence during the search by balancing the exploration and exploitation processes. These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. This paper presents a systematic literature review (SLR) on applications of four swarm intelligence algorithms i.e., grey wolf optimization (GWO), whale optimization algorithms (WOA), Harris hawks optimizer (HHO), and moth-flame optimizer (MFO) in the field of software engineering. It presents an in-depth study of these metaheuristics’ adoption in the field of software engineering. This SLR is mainly comprised of three phases such as planning, conducting, and reporting. This study covers all related studies published from 2014 up to 2022. The study shows that applications of the selected metaheuristics have been utilized in various fields of software engineering especially software testing, software defect prediction, and software reliability. The study also points out some of the areas where applications of these swarm intelligence algorithms can be utilized. This study may act as a guideline for researchers in improving the current state-of-the-art on generally adopting these metaheuristics in software engineering.

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Section: Applications Of Gwo Woa Hho and Mfo In Softwarementioning

confidence: 99%

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Dataset pre-processing eliminates irrelevant and redundant attributes from the original dataset (Tsai and Chen, 2010), thereby lessening the dimensionality, computational complexity, and enhancing interpretation and visualization efforts of the dataset. Several studies have investigated the effectiveness of feature selection methods on the performance of defect prediction models (Almayyan, 2021). Linear Discriminant Analysis (LDA) is used for feature dimension reduction and rank analysis (Dada et al 2021).…”

Section: Dataset Description and Pre-processingmentioning

confidence: 99%

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

Inyang¹,

Adeoye²,

Udo³

et al. 2022

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In today's society, almost all human endeavours depend on software products. Lack of quality software is one of the software industry's most important problems. Hence, it would be beneficial to access the quality of software to improve and enhance software products while increasing customer satisfaction. This paper assesses software product quality using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) software quality (SQ) framework was adopted in this work. Dimension reduction of the product metric category dataset and the entire PM dataset was conducted using linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian and coarse gaussian) were used to model each classifier. The combinations of the results from the multiple SVMs used AdaBoost, bagging, and random subspace ensemble methods for the assessment of SQ. All three ensemble learning methods performed better than the individual SVM, however, the bagging stood out with an accuracy of 93.0%. Hence, it was adopted in the fusion of the SVM results and classification of SQ into classes. Results from the confusion matrix and receivers’ operating characteristics were greater than 97.99% and confirm significant improvements with an ensemble of homogenous classifiers based on SVM.

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Towards Predicting Software Defects with Clustering Techniques

Cited by 2 publications

References 18 publications

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

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