PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS
Software plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to t...
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| Format: | Article |
| Language: | English |
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Faculty of Engineering, University of Kufa
2025-07-01
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| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/16641 |
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| _version_ | 1849390596161011712 |
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| author | Raghda Azad Hasan Ibrahim Ahmed Saleh |
| author_facet | Raghda Azad Hasan Ibrahim Ahmed Saleh |
| author_sort | Raghda Azad Hasan |
| collection | DOAJ |
| description | Software plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to the limited time and budget specified. Therefore, most software development companies tend to use machine learning for prediction. With the presence of software defects that contribute to improving the quality and safety of the software produced, this is done by relying on and using records, previous projects, and available data. this paper proposed machine learning and ensemble learning suite to predict software anomalies. The evaluated approach is for models in the PROMISE real-word dataset repository containing 5 projects (Turkish company SOFTLAB). The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. Both adaboost , stack achieved prediction accuracy about 99.2% when implemented on the ar5 dataset.
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| format | Article |
| id | doaj-art-170b29b87db848f0be4f8ceecb042b34 |
| institution | Kabale University |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-170b29b87db848f0be4f8ceecb042b342025-08-20T03:41:31ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-07-01160363965710.30572/2018/KJE/160336PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODSRaghda Azad Hasan0Ibrahim Ahmed Saleh1Software Department, Faculty of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq. Department of Software, Faculty of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq. Software plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to the limited time and budget specified. Therefore, most software development companies tend to use machine learning for prediction. With the presence of software defects that contribute to improving the quality and safety of the software produced, this is done by relying on and using records, previous projects, and available data. this paper proposed machine learning and ensemble learning suite to predict software anomalies. The evaluated approach is for models in the PROMISE real-word dataset repository containing 5 projects (Turkish company SOFTLAB). The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. Both adaboost , stack achieved prediction accuracy about 99.2% when implemented on the ar5 dataset. https://journal.uokufa.edu.iq/index.php/kje/article/view/16641software engineeringsoftware defect predictionensemble learningrandom forestdecision treeboostingstacking |
| spellingShingle | Raghda Azad Hasan Ibrahim Ahmed Saleh PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ software engineering software defect prediction ensemble learning random forest decision tree boosting stacking |
| title | PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS |
| title_full | PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS |
| title_fullStr | PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS |
| title_full_unstemmed | PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS |
| title_short | PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS |
| title_sort | prediction of software anomalies methods based on ensemble learning methods |
| topic | software engineering software defect prediction ensemble learning random forest decision tree boosting stacking |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/16641 |
| work_keys_str_mv | AT raghdaazadhasan predictionofsoftwareanomaliesmethodsbasedonensemblelearningmethods AT ibrahimahmedsaleh predictionofsoftwareanomaliesmethodsbasedonensemblelearningmethods |