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Volume 12
Issue 22
10.3390/app122211694
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Review

Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques

by
Mahmudul Hoque Mahmud
1,
Md. Tanzirul Haque Nayan
1,
Dewan Md. Nur Anjum Ashir
1 and
Md Alamgir Kabir
2,*
1
Department of Computer Science, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, Bangladesh
2
Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Malardalen University, Hogskoleplan 1, 722 20 Vasteras, Sweden
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(22), 11694; https://doi.org/10.3390/app122211694
Submission received: 30 October 2022 / Revised: 11 November 2022 / Accepted: 14 November 2022 / Published: 17 November 2022
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Abstract

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.
Keywords: systematic literature review; software risk; software risk prediction model; machine learning model; review systematic literature review; software risk; software risk prediction model; machine learning model; review

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MDPI and ACS Style

Mahmud, M.H.; Nayan, M.T.H.; Ashir, D.M.N.A.; Kabir, M.A. Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques. Appl. Sci. 2022, 12, 11694. https://doi.org/10.3390/app122211694

AMA Style

Mahmud MH, Nayan MTH, Ashir DMNA, Kabir MA. Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques. Applied Sciences. 2022; 12(22):11694. https://doi.org/10.3390/app122211694

Chicago/Turabian Style

Mahmud, Mahmudul Hoque, Md. Tanzirul Haque Nayan, Dewan Md. Nur Anjum Ashir, and Md Alamgir Kabir. 2022. "Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques" Applied Sciences 12, no. 22: 11694. https://doi.org/10.3390/app122211694

APA Style

Mahmud, M. H., Nayan, M. T. H., Ashir, D. M. N. A., & Kabir, M. A. (2022). Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques. Applied Sciences, 12(22), 11694. https://doi.org/10.3390/app122211694

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