Hybrid SMOTE-Ensemble Approach for Software Defect Prediction

Alsawalqah, Hamad; Faris, Hossam; Aljarah, Ibrahim; Alnemer, Loai M.; Alhindawi, Nouh

doi:10.1007/978-3-319-57141-6_39

Cited by 33 publications

(24 citation statements)

References 22 publications

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“…In the context of binary class classification, hundreds of different defect prediction models have been published. To build these models, researchers have used various classification techniques to build the defect prediction models such as Logistic Regression [24], NB [23], SVM [26], ANN [39], Genetic Programming [40], Ant Colony Optimization [14], Particle Swarm Optimization [41], RF [42], Case Based Reasoning [30], DT [25], ensemble methods [16,28,29,43,44], EM [31], Fuzzy clustering [33], K-means clustering [32], Association Rule Mining [45], and the Artificial Immune Systems [46,47].…”

Section: Related Workmentioning

confidence: 99%

“…Effective defect prediction could help test managers locate bugs and facilitate the allocation of limited SQA resources optimally and economically; thus, it has become an extremely important research topic [7][8][9][10][11][12]. Commonly, a prediction model is used to predict the defective software modules in one of the three categories: binary class classification of defects [13][14][15][16], number of defects/defect density prediction [17][18][19][20], and severity of defect prediction [21,22]. Among them, the binary class classification is the most frequently used types of prediction scheme, where software modules having one or more defects are marked as defected and modules having zero defects are marked as non-defected.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the binary class classification is the most frequently used types of prediction scheme, where software modules having one or more defects are marked as defected and modules having zero defects are marked as non-defected. In this type of defect prediction schema, researchers have explored the use of various classification techniques, including statistical techniques such as Naïve Bayes (NB) [23] and Logistic Regression [24]; supervised techniques such as Decision Tree (DT) [25], Support Vector Machine (SVM) [26], ensemble methods [16,[27][28][29], and Case Based Reasoning [30]; semi supervised techniques such as Expectation Maximization (EM) [31]; and unsupervised techniques such as K-means clustering [32] and Fuzzy clustering [33]. Most of the studies in the literature have used statistical and supervised learning techniques [34].…”

Section: Introductionmentioning

confidence: 99%

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Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

et al. 2020

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Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

et al. 2020

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“…Firstly, we compared the performance of different supervised and Ensemble methods on the oversampled training data, while other works such as Kalai Magal et al [28] and Venkata et al [9] focused on the impact of feature selection and attribute reduction on the performance of classifiers. Secondly, a very similar study to our approach presented in this paper was conducted by Alsawalqah et al [63], where they studied the impact of SMOTE on the Adaboost ensemble method with J48 as a base classifier. Their findings demonstrated that SMOTE can help to boost the performance of the ensemble method on four NASA datasets.…”

Section: Related Workmentioning

confidence: 90%

Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study

Alsaeedi¹,

Khan²

2019

JSEA

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An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.

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“…Alsawalqah et al [25] tested the effect of SMOTE as a base classifier on the Adaboost ensemble system with J48. Our findings showed that SMOTE could help boost the ensemble method's output on four NASA datasets.…”

Section: Related Researchmentioning

confidence: 99%

Hybrid Ensemble Learning Technique for Software Defect Prediction

Khan¹

2020

IJMECS

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The reliability of software depends on its ability to function without error. Unfortunately, errors can be generated during any phase of software development. In the field of software engineering, the prediction of software defects during the initial stages of development has therefore become a top priority. Scientific data are used to predict the software's future release. Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects. During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed. This paper explores and compares well-known supervised machine learning and hybrid ensemble classifiers in eight PROMISE datasets. The experimental results showed that AdaBoost support vector machines and bagging support vector machines were the best performing classifiers in Accuracy, AUC, recall and F-measure.

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Hybrid SMOTE-Ensemble Approach for Software Defect Prediction

Cited by 33 publications

References 22 publications

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study

Hybrid Ensemble Learning Technique for Software Defect Prediction

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