Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools
The development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10798423/ |
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| author | Mohd Mustaqeem Mahfooz Alam Suhel Mustajab Faisal Alshanketi Shadab Alam Mohammed Shuaib |
| author_facet | Mohd Mustaqeem Mahfooz Alam Suhel Mustajab Faisal Alshanketi Shadab Alam Mohammed Shuaib |
| author_sort | Mohd Mustaqeem |
| collection | DOAJ |
| description | The development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software defects after release is crucial. While binary classification methods have been commonly used for this purpose, recent Artificial Intelligence (AI) advancements offer new opportunities for software teams to create more robust software. To address challenges in Software Defect Prediction (SDP), we conducted a thorough bibliographic survey of 79 research articles from the year 2011 to 2023 that examined previous models, datasets, data validation techniques, defect detection, prediction methods, and SDP tools. The survey revealed that previous research often lacked appropriate datasets with the necessary characteristics and data validation methods. Additionally, many standard datasets suffer from a lack of labels, which hinders effective defect detection. Systematic literature reviews on SDP are scarce, further emphasizing the importance of this study. Based on the findings, we provide crucial recommendations for designing effective SDP models and tools. The proposed survey outlines an architecture for constructing SDP datasets with the appropriate characteristics, as well as multi-label classification and data validation methodologies for software defects. This approach aims to enhance SDP research and contribute to the development of high-quality software products by improving defect prediction accuracy. |
| format | Article |
| id | doaj-art-3d951360234845588578c00a7e24cb25 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3d951360234845588578c00a7e24cb252025-01-03T00:01:36ZengIEEEIEEE Access2169-35362025-01-011386690310.1109/ACCESS.2024.351741910798423Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and ToolsMohd Mustaqeem0https://orcid.org/0000-0001-5055-5969Mahfooz Alam1https://orcid.org/0000-0003-0668-9796Suhel Mustajab2https://orcid.org/0000-0002-9969-6110Faisal Alshanketi3https://orcid.org/0000-0001-5982-5937Shadab Alam4https://orcid.org/0000-0003-0504-4515Mohammed Shuaib5https://orcid.org/0000-0001-6657-2587Department of Computer Science, Aligarh Muslim University, Aligarh, IndiaDepartment of Computer Science, Aligarh Muslim University, Aligarh, IndiaDepartment of Computer Science, Aligarh Muslim University, Aligarh, IndiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaThe development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software defects after release is crucial. While binary classification methods have been commonly used for this purpose, recent Artificial Intelligence (AI) advancements offer new opportunities for software teams to create more robust software. To address challenges in Software Defect Prediction (SDP), we conducted a thorough bibliographic survey of 79 research articles from the year 2011 to 2023 that examined previous models, datasets, data validation techniques, defect detection, prediction methods, and SDP tools. The survey revealed that previous research often lacked appropriate datasets with the necessary characteristics and data validation methods. Additionally, many standard datasets suffer from a lack of labels, which hinders effective defect detection. Systematic literature reviews on SDP are scarce, further emphasizing the importance of this study. Based on the findings, we provide crucial recommendations for designing effective SDP models and tools. The proposed survey outlines an architecture for constructing SDP datasets with the appropriate characteristics, as well as multi-label classification and data validation methodologies for software defects. This approach aims to enhance SDP research and contribute to the development of high-quality software products by improving defect prediction accuracy.https://ieeexplore.ieee.org/document/10798423/Software defect predictionclassificationartificial intelligencemachine learningstatistical validationbibliographic survey |
| spellingShingle | Mohd Mustaqeem Mahfooz Alam Suhel Mustajab Faisal Alshanketi Shadab Alam Mohammed Shuaib Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools IEEE Access Software defect prediction classification artificial intelligence machine learning statistical validation bibliographic survey |
| title | Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools |
| title_full | Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools |
| title_fullStr | Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools |
| title_full_unstemmed | Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools |
| title_short | Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools |
| title_sort | comprehensive bibliographic survey and forward looking recommendations for software defect prediction datasets validation methodologies prediction approaches and tools |
| topic | Software defect prediction classification artificial intelligence machine learning statistical validation bibliographic survey |
| url | https://ieeexplore.ieee.org/document/10798423/ |
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