Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive Model

Pandey, Sushant Kumar; Mishra, R. B.; Triphathi, Anil Kumar

doi:10.1016/j.procs.2018.05.071

Cited by 25 publications

(13 citation statements)

References 25 publications

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“…The literature reveals that ML algorithms can effectively tackle the SFP problem where several techniques have been proposed for detecting faults in software modules. Examples of ML-based SFP approaches include LRM [46], FC [47], CR [48], DT [49], NB [16], [50], ANN [51], RF [50], BN [52] and SVM [17], [53]. Moreover, for evaluating SFP techniques, various publicly available datasets are used.…”

Section: Review Of Related Work a Software Fault Predictionmentioning

confidence: 99%

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

et al. 2021

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Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

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Section: Review Of Related Work a Software Fault Predictionmentioning

confidence: 99%

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

et al. 2021

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“…They have reported that the most studied searchbased techniques used in defect prediction are the Artificial Immune Recognition System (AIRS), Ant Colony Optimization (ACO), Genetic Programming (GP), Evolutionary Programming (EP), Evolutionary Subgroup Discovery (ESD), GA, and Gene Expression Programming (GeP) and Particle Swarm Optimization (PSO). Pandey et al [36] investigated the effect of some Bayesian network (BN) and classifier for bug prediction on NASA and Eclipse datasets. Receiver operating characteristics (ROC) and AUC performance measures are used to measure various parameters performance of the classifiers.…”

Section: Related Workmentioning

confidence: 99%

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

et al. 2020

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Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyperparameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyper-parameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used. INDEX TERMS Artificial immune network (AIN), artificial immune system (AIS), hyper-parameter optimization, optimized artificial immune network (opt-aiNet), software bug prediction (SBP).

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“…[52] combined dimension reduction techniques with SVM, which leads to achieving significant good results. Recent works by Pandey et al [53] and Mori and Uchihira [54] proposed an augmented‐based Naive Bayes model and also stated the trade‐off between accuracy and interpretability.…”

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 Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive Model

Cited by 25 publications

References 25 publications

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

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

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