Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning
Deep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the predicti...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10485281/ |
| _version_ | 1797217404631121920 |
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| author | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud |
| author_facet | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud |
| author_sort | Ahmad Muhaimin Ismail |
| collection | DOAJ |
| description | Deep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the prediction performance. In software defect prediction, false positives occur when the prediction model incorrectly predicts code changes to be defective. Consequently, developers waste time and resources on non-existent defects. This paper advocates for employing DQN in software defect prediction, focusing on minimizing false positives and maximizing the prediction performance. Throughout the training phase, the model learns to predict defect-prone following a reward policy aimed at reducing false results. Experimental findings show that the proposed DQN outperforms baseline classifier, improving the prediction accuracy of true defects by up to 27% when using only 20% efforts. The results show that the effectiveness of DQN in tackling false positives, thereby emphasizing the significance of incorporating dynamic reward in predicting software defects. |
| first_indexed | 2024-04-24T12:01:19Z |
| format | Article |
| id | doaj.art-dec7a1c5a9eb4be4abac954c05ece237 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T12:01:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dec7a1c5a9eb4be4abac954c05ece2372024-04-08T23:00:41ZengIEEEIEEE Access2169-35362024-01-0112475684758010.1109/ACCESS.2024.338299110485281Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement LearningAhmad Muhaimin Ismail0https://orcid.org/0009-0001-2273-8917Siti Hafizah Ab Hamid1https://orcid.org/0000-0001-9598-8813Asmiza Abdul Sani2https://orcid.org/0000-0002-1384-4005Nur Nasuha Mohd Daud3https://orcid.org/0000-0003-4683-0926Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDeep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the prediction performance. In software defect prediction, false positives occur when the prediction model incorrectly predicts code changes to be defective. Consequently, developers waste time and resources on non-existent defects. This paper advocates for employing DQN in software defect prediction, focusing on minimizing false positives and maximizing the prediction performance. Throughout the training phase, the model learns to predict defect-prone following a reward policy aimed at reducing false results. Experimental findings show that the proposed DQN outperforms baseline classifier, improving the prediction accuracy of true defects by up to 27% when using only 20% efforts. The results show that the effectiveness of DQN in tackling false positives, thereby emphasizing the significance of incorporating dynamic reward in predicting software defects.https://ieeexplore.ieee.org/document/10485281/Software defect predictionDeep Q-Networkfalse positives |
| spellingShingle | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning IEEE Access Software defect prediction Deep Q-Network false positives |
| title | Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning |
| title_full | Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning |
| title_fullStr | Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning |
| title_full_unstemmed | Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning |
| title_short | Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning |
| title_sort | toward reduction in false positives just in time software defect prediction using deep reinforcement learning |
| topic | Software defect prediction Deep Q-Network false positives |
| url | https://ieeexplore.ieee.org/document/10485281/ |
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