BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques

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

doi:10.1016/j.eswa.2019.113085

Cited by 92 publications

(39 citation statements)

References 64 publications

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“…Pandey et al [29] proposed a rudimentary classificationbased framework Bug Prediction using Deep representation and Ensemble learning (BPDET) techniques for the software bug prediction (SBP) model. Staked de-noising auto-encoder (SDA) was used for the deep representation of software metrics.…”

Section: Related Workmentioning

confidence: 99%

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Gupta

Rajnish

Bhattacherjee

2021

Scientific Programming

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Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially desired when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more towards the modules identified to be fault prone. To be able to build an efficient deep neural network model, it is important that the parameters such as number of hidden layers, number of nodes in each layer, and training details such as learning rate and regularization methods be investigated in detail. The objective of this paper is to show the importance of hyperparameter tuning in developing efficient deep neural network models for predicting fault proneness of software modules and to compare the results with other machine learning algorithms. It is shown that the proposed model outperforms the other algorithms in most cases.

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Section: Related Workmentioning

confidence: 99%

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Gupta

Rajnish

Bhattacherjee

2021

Scientific Programming

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“…They have reported that 97% accuracy is achieved in RF and RF out-performed than other machine learning algorithms. Pandey et al [41] used a combined approach of ensemble learning (EL) and deep representation (DR) namely bug prediction using deep representation and ensemble learning technique (BPDET) for SBP on 12 NASA datasets. The class imbalance issue in the dataset is addressed by the SMOTE sampling technique.…”

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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“…Bennin et al [63] applied five statistical methods over six sampling techniques on ten public datasets and found extensively satisfying results. Tong et al [48] and Pandey et al [64, 65] applied a dropout regularisation technique to avoid the overfitting problem. Khoshgoftaar and Allen [66] suggested a tree‐based approach to avoid overfitting problems.…”

Section: Related Workmentioning

confidence: 99%

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

2020

Self Cite

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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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BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques

Cited by 92 publications

References 64 publications

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

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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