A Classification Model for Software Bug Prediction Based on Ensemble Deep Learning Approach Boosted with SMOTE Technique

Thaher, Thaer; Khamayseh, Faisal

doi:10.1007/978-981-33-6984-9_9

Advances in Intelligent Systems and Computing

2021

DOI: 10.1007/978-981-33-6984-9_9

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A Classification Model for Software Bug Prediction Based on Ensemble Deep Learning Approach Boosted with SMOTE Technique

Thaer Thaher

Faisal Khamayseh

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Cited by 3 publications

References 21 publications

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Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Mafarja

Thaher

Al-Betar³

et al. 2023

Appl Intell

Self Cite

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Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms’ performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

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Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Mafarja

Thaher

Al-Betar³

et al. 2023

Appl Intell

Self Cite

View full text Add to dashboard Cite

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Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term Memory

Siva,

Hariharan

et al. 2023

Wireless Pers Commun

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In the software maintenance and development process, software bug detection is an essential problem because it is related to complete software success. It is recommended to begin anticipating defects at the early stages of creation rather than during the assessment process due to the high expense of fixing the found bugs. The early stage software bug detection is used to enhance software efficiency, reliability, and software quality. Nevertheless, creating a reliable bug-forecasting system is a difficult challenge. Therefore, in this paper, an efficient, software bug forecast is developed. The presented technique consists of three stages namely, pre-processing, feature selection, and bug prediction. At first, the input datasets are pre-processed to eliminate the identical data from the dataset. After the pre-processing, the important features are selected using an adaptive artificial jelly optimization algorithm (A2JO) to eliminate the possibility of overfitting and reduce the complexity. Finally, the selected features are given to the long short-term memory (LSTM) classifier to predict whether the given data is defective or non-defective. In this paper, investigations are shown on visibly obtainable bug prediction datasets namely, promise and NASA which is a repository for most open-source software. The efficiency of the presented approach is discussed based on various metrics namely, accuracy, F- measure, G-measure, and Matthews Correlation Coefficient (MCC). The experimental result shows our proposed method achieved the extreme accuracy of 93.41% for the Promise dataset and 92.8% for the NASA dataset.

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An Improved and Optimized Random Forest Based Approach to Predict the Software Faults

Thomas,

Kaliraj

2024

SN COMPUT. SCI.

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Effective software fault prediction is crucial for minimizing errors during software development and preventing subsequent failures. This research introduces an enhanced Random Forest-based approach for predicting software faults, specifically focusing on the NASA JM1 dataset. The dataset comprises 21 software metrics indicating the presence or absence of faults in a module, and it is utilized to evaluate the proposed approach. The study delves into the intricacies of the NASA dataset, detailing the cleaning process and addressing class imbalance through Synthetic Minority Over-sampling Technique (SMOTE). The core of our approach involves the implementation and fine-tuning of the Random Forest classifier, with a specific focus on optimizing hyperparameters to enhance predictive accuracy. In comparative evaluations with standard machine learning models, our proposed approach demonstrated superior performance, achieving an accuracy of 82.96% and an F1 score of 89.53%. Notably, we emphasize the significance of software defects and their potential to cause failures and crashes during software development, leading to substantial organizational losses. The paper provides a comprehensive examination of different aspects of the machine learning model, offering detailed insights, examples, and illustrative figures to enhance the understanding of our proposed approach.

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A Classification Model for Software Bug Prediction Based on Ensemble Deep Learning Approach Boosted with SMOTE Technique

Cited by 3 publications

References 21 publications

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term Memory

An Improved and Optimized Random Forest Based Approach to Predict the Software Faults

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