Improvement in Software Defect Prediction Outcome Using Principal Component Analysis and Ensemble Machine Learning Algorithms

Dhamayanthi, N.; Lavanya, B.

doi:10.1007/978-3-030-03146-6_44

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The results showed that CSL on the hybrid SMOTE-AdaBoost method outperformed other methods in terms of G-mean. In [52], a framework in which PCA was used for feature selection and ensemble learning for classification was proposed. Four ensembles were employed: RF, Adaboost, bagging, and classification via regression.…”

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Software Defect Prediction?mentioning

confidence: 99%

“…[5] performed analysis of FS and three ensemble learning methods [6] combined FS and Data Balancing (DB) with ensemble techniques [7] proposed SmoteNDBoost and RusNDBoost [63] compared the bagging, boosting, and stacking ensembles. 11 base classifiers were used [64] RF was combined with feature selection and data sampling [65] analyze whether different classifiers identify the same defects or not using RF, NB, RPart, and SVM 2018 [8] analyzed ensembles of weighted randomized majority voting techniques [9] proposed SDAEsTSE model [12] analyzed performance model with and without applying SMOTE, AdaBoost, and Bagging [41] Proposed PBIL-Auto-Ens technique [42] proposed ensemble ROS, MWM, and FIDos methods with RF as base classifier [47] proposed multi-objective optimization for ensemble classification [52] proposed framework based on PCA in FS and RF, Adaboost, bagging, and classification via regression ensembles [55] proposed enhancement in the SMOTE-Ensemble approach using cost-sensitive learning (CSL) [66] proposed deep super learner (DSL) 2019 [67] compared Adaboost, Bagging, RSM, RF, and Vote ensembles [68] ten ensemble classifiers were compared to baseline classifiers 2020…”

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Software Defect Prediction?mentioning

confidence: 99%

“…In another study [50], performance measure recall, precision, accuracy, and F-value were used to evaluate and compare results. Other performance measures that were frequently applied include accuracy in [5][6], [8], [44], [49], [52], and [64]; PofB20 by [10]; and the Kolmogorov-Smirnov (K-S) test in [11]. In [41], the results of Auto-Weka and Auto-PBIL-Ens models were compared using mean error rates.…”

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

“…Fig 5 shows the distribution of primary studies over machine learning tools and libraries. In our selected primary studies, we determined that Waikato Environment for Knowledge Analysis (WEKA) is the most frequently utilized machine learning tool to explore, visualize and perform classification over data [1], [3][4], [6], [8][9], [11][12], [41], [43], [45], [50][51][52][53][54][55], [58], [60], [62][63][64], [67][68], [73]. Other tools include MATLAB [9], [11], [47], [48] and sklearn [7], which is a python machine learning library and LIBSVM [56].…”

Section: Rq3 : What Are the Most Commonly Employed Tools To Implement Ensemble Learning Techniques?mentioning

confidence: 99%

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Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

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Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions that cover the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that feature selection and data sampling were important pre-processing steps that improve the performance of ensemble classifiers.

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Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Software Defect Prediction?mentioning

confidence: 99%

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Software Defect Prediction?mentioning

confidence: 99%

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

Section: Rq3 : What Are the Most Commonly Employed Tools To Implement Ensemble Learning Techniques?mentioning

confidence: 99%

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Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

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“…There has been much research work [26, 27] going on over SDP over the past three decades. Naive Bayes [28–31], support vector machine (SVM) [32–34], classification and regression trees [35, 36], AdaBoost [37–39], random forest [40–42], artificial neural network as defect prediction [43, 44], and development effort estimation [45]. Multilayer perceptrons (MLPs) [46, 47] are the most widely used classifiers in defect prediction.…”

Section: Related Workmentioning

confidence: 99%

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

2020

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Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

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Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

Dhamayanthi

Lavanya

2019

Proceedings of International Conference on Computational Intelligence and Data Engineering

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Improvement in Software Defect Prediction Outcome Using Principal Component Analysis and Ensemble Machine Learning Algorithms

Cited by 9 publications

References 9 publications

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

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