A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction
Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/11/12/246 |
| _version_ | 1811286160619601920 |
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| author | Saleh Albahli |
| author_facet | Saleh Albahli |
| author_sort | Saleh Albahli |
| collection | DOAJ |
| description | Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions. |
| first_indexed | 2024-04-13T02:55:33Z |
| format | Article |
| id | doaj.art-002c98a4d96c447ba5b29ce38fd91e7c |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-04-13T02:55:33Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-002c98a4d96c447ba5b29ce38fd91e7c2022-12-22T03:05:39ZengMDPI AGFuture Internet1999-59032019-11-01111224610.3390/fi11120246fi11120246A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect PredictionSaleh Albahli0Department of Information Technology, College of Computer, Qassim University, Buraidah 51452, Saudi ArabiaSince the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.https://www.mdpi.com/1999-5903/11/12/246deep neural networkunlabeled datasetjust-in-time defect predictionunsupervised prediction |
| spellingShingle | Saleh Albahli A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction Future Internet deep neural network unlabeled dataset just-in-time defect prediction unsupervised prediction |
| title | A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction |
| title_full | A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction |
| title_fullStr | A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction |
| title_full_unstemmed | A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction |
| title_short | A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction |
| title_sort | deep ensemble learning method for effort aware just in time defect prediction |
| topic | deep neural network unlabeled dataset just-in-time defect prediction unsupervised prediction |
| url | https://www.mdpi.com/1999-5903/11/12/246 |
| work_keys_str_mv | AT salehalbahli adeepensemblelearningmethodforeffortawarejustintimedefectprediction AT salehalbahli deepensemblelearningmethodforeffortawarejustintimedefectprediction |