FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration
The increasing complexity of software projects makes it difficult to predict risks in software requirements, which is a crucial and essential part of the Software Development Life Cycle (SDLC). The failure of a software project may occur from an inability to appropriately anticipate such risks. Beca...
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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/10506837/ |
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| author | Muhammad Binsawad Bilal Khan |
| author_facet | Muhammad Binsawad Bilal Khan |
| author_sort | Muhammad Binsawad |
| collection | DOAJ |
| description | The increasing complexity of software projects makes it difficult to predict risks in software requirements, which is a crucial and essential part of the Software Development Life Cycle (SDLC). The failure of a software project may occur from an inability to appropriately anticipate such risks. Because it is the first stage of any software project, risk prediction has a greater significance in software requirements. Thus, ForExPlusPlus (FEPP), a novel model for risk prediction in software requirements, is proposed in this work. Standard models such as K-nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Model Tree (LMT), Random Forest (RF), and Support Vector Machine (SVM) are used to benchmark the suggested model. The dataset from the Zenodo repository is used to train these models, and standard assessment criteria are used to evaluate the results. The accuracy analysis of the models is assessed critically using the precision, F-measure (FM), and Mathew’s correlation coefficient (MCC), as well as the error rate using the Kappa Statistic (KS) and Mean Absolute Error (MAE). The suggested FEPP performs better overall, with an accuracy of 96.84%, whereas KNN performs the worst, with an accuracy of 50.99%. |
| format | Article |
| id | doaj-art-8e62fe4586f14d1b83b4faafcddec877 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8e62fe4586f14d1b83b4faafcddec8772025-08-20T02:33:51ZengIEEEIEEE Access2169-35362024-01-0112598515986010.1109/ACCESS.2024.339228310506837FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class IntegrationMuhammad Binsawad0https://orcid.org/0000-0003-0915-7058Bilal Khan1https://orcid.org/0000-0002-6816-3776Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, City University of Science and Information Technology, Peshawar, PakistanThe increasing complexity of software projects makes it difficult to predict risks in software requirements, which is a crucial and essential part of the Software Development Life Cycle (SDLC). The failure of a software project may occur from an inability to appropriately anticipate such risks. Because it is the first stage of any software project, risk prediction has a greater significance in software requirements. Thus, ForExPlusPlus (FEPP), a novel model for risk prediction in software requirements, is proposed in this work. Standard models such as K-nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Model Tree (LMT), Random Forest (RF), and Support Vector Machine (SVM) are used to benchmark the suggested model. The dataset from the Zenodo repository is used to train these models, and standard assessment criteria are used to evaluate the results. The accuracy analysis of the models is assessed critically using the precision, F-measure (FM), and Mathew’s correlation coefficient (MCC), as well as the error rate using the Kappa Statistic (KS) and Mean Absolute Error (MAE). The suggested FEPP performs better overall, with an accuracy of 96.84%, whereas KNN performs the worst, with an accuracy of 50.99%.https://ieeexplore.ieee.org/document/10506837/Software risk predictionForExPlusPlusrequirements engineeringZenodo datasets |
| spellingShingle | Muhammad Binsawad Bilal Khan FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration IEEE Access Software risk prediction ForExPlusPlus requirements engineering Zenodo datasets |
| title | FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration |
| title_full | FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration |
| title_fullStr | FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration |
| title_full_unstemmed | FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration |
| title_short | FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration |
| title_sort | fepp advancing software risk prediction in requirements engineering through innovative rule extraction and multi class integration |
| topic | Software risk prediction ForExPlusPlus requirements engineering Zenodo datasets |
| url | https://ieeexplore.ieee.org/document/10506837/ |
| work_keys_str_mv | AT muhammadbinsawad feppadvancingsoftwareriskpredictioninrequirementsengineeringthroughinnovativeruleextractionandmulticlassintegration AT bilalkhan feppadvancingsoftwareriskpredictioninrequirementsengineeringthroughinnovativeruleextractionandmulticlassintegration |