International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Software Risk Prediction Through the Use of Machine Learning: Review

Rabab T. MahmoodDepartment of Software Engineering, Computer Science and Mathematics College, University of Mosul, Mosul, IraqIbrahim A. SalehDepartment of Software Engineering, Computer Science and Mathematics College, University of Mosul, Mosul, Iraq

Vol 7 No 2 (2023): Volume 7, Issue 2, February 2023 | Pages: 42-49

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 16-02-2023

doi Logo doi.org/10.47001/IRJIET/2023.702006

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Abstract

Software engineering and data science require strong programming skills. Software engineering focuses more on construction, functionality, and features, while software risk forecasting focuses more on data collection and analysis. A high level of system functionality is one of the basic needs of software development projects. One of the main characteristics that directly affects the effectiveness of software systems is the prediction of risks. Organizations can make decisions about potential solutions and improvements by using the ability to identify software systems risks through early recognition of expected failures. Inaccurate risk assessments may result in poor system performance and thus reveal its reliability. This research focuses on reviewing mechanisms for predicting early failure in software project risk assessment. Various ML machine learning techniques are used. The aim of the study is to review experience-based risk assessment models that use historical failure data from several past program projects as training data to accurately assess the risks of program initiatives. This study covers software project risk prediction models that are generally applied to all software projects throughout the software development process, helping advance the evolution of software systems.

Keywords

Software Development Life Cycle (SDLC), Software Risk (SR), Machine Learning (ML), Risks Supervision (RM), Software Risk Management (SRM), Software Engineering (SE)


Citation of this Article

Rabab T. Mahmood, Ibrahim A. Saleh, “Software Risk Prediction Through the Use of Machine Learning: Review” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 2, pp 42-49, February 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.702006

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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