Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms
In today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in main...
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MDPI AG
2025-07-01
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| author | Ayesha Siddika Momotaz Begum Fahmid Al Farid Jia Uddin Hezerul Abdul Karim |
| author_facet | Ayesha Siddika Momotaz Begum Fahmid Al Farid Jia Uddin Hezerul Abdul Karim |
| author_sort | Ayesha Siddika |
| collection | DOAJ |
| description | In today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. In semi-supervised learning, we tested are autoencoders, semi-supervised support vector machines, and generative adversarial networks. For self-supervised learning, we utilized are autoencoder, simple framework for contrastive learning of representations, and bootstrap your own latent. After comparing the performance of each machine learning algorithm, we identified the most effective one. Among these, the gradient boosting AdaBoost classifier demonstrated superior performance based on an accuracy of 90%, closely followed by the AdaBoost classifier at 89%. Finally, we applied ensemble methods to predict software defects, leveraging the collective strengths of these diverse approaches. This enables software developers to significantly enhance defect prediction accuracy, thereby improving overall system robustness and reliability. |
| format | Article |
| id | doaj-art-aec7c1212d2c460586f2486ae69141db |
| institution | Kabale University |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-aec7c1212d2c460586f2486ae69141db2025-08-20T03:32:26ZengMDPI AGEng2673-41172025-07-016716110.3390/eng6070161Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning ParadigmsAyesha Siddika0Momotaz Begum1Fahmid Al Farid2Jia Uddin3Hezerul Abdul Karim4Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, BangladeshDepartment of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, BangladeshCentre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, MalaysiaArtificial Intelligence and Big Data Department, Endicott College, Woosong University, Daejeon 34600, Republic of KoreaCentre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, MalaysiaIn today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. In semi-supervised learning, we tested are autoencoders, semi-supervised support vector machines, and generative adversarial networks. For self-supervised learning, we utilized are autoencoder, simple framework for contrastive learning of representations, and bootstrap your own latent. After comparing the performance of each machine learning algorithm, we identified the most effective one. Among these, the gradient boosting AdaBoost classifier demonstrated superior performance based on an accuracy of 90%, closely followed by the AdaBoost classifier at 89%. Finally, we applied ensemble methods to predict software defects, leveraging the collective strengths of these diverse approaches. This enables software developers to significantly enhance defect prediction accuracy, thereby improving overall system robustness and reliability.https://www.mdpi.com/2673-4117/6/7/161software defectsemi-supervised learningself-supervised learningsupervised learningensemblemachine learning |
| spellingShingle | Ayesha Siddika Momotaz Begum Fahmid Al Farid Jia Uddin Hezerul Abdul Karim Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms Eng software defect semi-supervised learning self-supervised learning supervised learning ensemble machine learning |
| title | Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms |
| title_full | Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms |
| title_fullStr | Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms |
| title_full_unstemmed | Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms |
| title_short | Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms |
| title_sort | enhancing software defect prediction using ensemble techniques and diverse machine learning paradigms |
| topic | software defect semi-supervised learning self-supervised learning supervised learning ensemble machine learning |
| url | https://www.mdpi.com/2673-4117/6/7/161 |
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