Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
The Software Development Life Cycle (SDLC) includes the phases used to develop software. During the phases of the SDLC, unexpected risks might arise due to a lack of knowledge, control, and time. The consequences are severe if the risks are not addressed in the early phases of SDLC. This study aims...
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-11-01
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| Colecção: | Applied Sciences |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2076-3417/12/22/11694 |
| _version_ | 1827645032257028096 |
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| author | Mahmudul Hoque Mahmud Md. Tanzirul Haque Nayan Dewan Md. Nur Anjum Ashir Md Alamgir Kabir |
| author_facet | Mahmudul Hoque Mahmud Md. Tanzirul Haque Nayan Dewan Md. Nur Anjum Ashir Md Alamgir Kabir |
| author_sort | Mahmudul Hoque Mahmud |
| collection | DOAJ |
| description | The Software Development Life Cycle (SDLC) includes the phases used to develop software. During the phases of the SDLC, unexpected risks might arise due to a lack of knowledge, control, and time. The consequences are severe if the risks are not addressed in the early phases of SDLC. This study aims to conduct a Systematic Literature Review (SLR) and acquire concise knowledge of Software Risk Prediction (SRP) from the published scientific articles from the year 2007 to 2022. Furthermore, we conducted a qualitative analysis of published articles on SRP. Some of the key findings include: (1) 16 articles are examined in this SLR to represent the outline of SRP; (2) Machine Learning (ML)-based detection models were extremely efficient and significant in terms of performance; (3) Very few research got excellent scores from quality analysis. As part of this SLR, we summarized and consolidated previously published SRP studies to discover the practices from prior research. This SLR will pave the way for further research in SRP and guide both researchers and practitioners. |
| first_indexed | 2024-03-09T18:29:49Z |
| format | Article |
| id | doaj.art-eb7f9102379c4ce88b1665fc58a3a620 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:49Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb7f9102379c4ce88b1665fc58a3a6202023-11-24T07:39:24ZengMDPI AGApplied Sciences2076-34172022-11-0112221169410.3390/app122211694Software Risk Prediction: Systematic Literature Review on Machine Learning TechniquesMahmudul Hoque Mahmud0Md. Tanzirul Haque Nayan1Dewan Md. Nur Anjum Ashir2Md Alamgir Kabir3Department of Computer Science, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, BangladeshDepartment of Computer Science, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, BangladeshDepartment of Computer Science, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, BangladeshArtificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Malardalen University, Hogskoleplan 1, 722 20 Vasteras, SwedenThe Software Development Life Cycle (SDLC) includes the phases used to develop software. During the phases of the SDLC, unexpected risks might arise due to a lack of knowledge, control, and time. The consequences are severe if the risks are not addressed in the early phases of SDLC. This study aims to conduct a Systematic Literature Review (SLR) and acquire concise knowledge of Software Risk Prediction (SRP) from the published scientific articles from the year 2007 to 2022. Furthermore, we conducted a qualitative analysis of published articles on SRP. Some of the key findings include: (1) 16 articles are examined in this SLR to represent the outline of SRP; (2) Machine Learning (ML)-based detection models were extremely efficient and significant in terms of performance; (3) Very few research got excellent scores from quality analysis. As part of this SLR, we summarized and consolidated previously published SRP studies to discover the practices from prior research. This SLR will pave the way for further research in SRP and guide both researchers and practitioners.https://www.mdpi.com/2076-3417/12/22/11694systematic literature reviewsoftware risksoftware risk prediction modelmachine learning modelreview |
| spellingShingle | Mahmudul Hoque Mahmud Md. Tanzirul Haque Nayan Dewan Md. Nur Anjum Ashir Md Alamgir Kabir Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques Applied Sciences systematic literature review software risk software risk prediction model machine learning model review |
| title | Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques |
| title_full | Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques |
| title_fullStr | Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques |
| title_full_unstemmed | Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques |
| title_short | Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques |
| title_sort | software risk prediction systematic literature review on machine learning techniques |
| topic | systematic literature review software risk software risk prediction model machine learning model review |
| url | https://www.mdpi.com/2076-3417/12/22/11694 |
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