Abioye, Temitope ElizabethArogundade, Oluwasefunmi TaleMisra, SanjayAkinwale, Adio T.Adeniran, Olusola JohnComputer Engineering2024-07-052024-07-0520202047-74732047-748110.1002/smr.22692-s2.0-85087209477https://doi.org/10.1002/smr.2269https://hdl.handle.net/20.500.14411/3060Misra, Sanjay/0000-0002-3556-9331; Ogunbiyi, Temitope Elizabeth/0000-0002-3373-396X; Arogundade, Oluwasefunmi/0000-0001-9338-491XSoftware risk management is a proactive decision-making practice with processes, methods, and tools for managing risks in a software project. Many existing techniques for software project risk management are textual documentation with varying perspectives that are nonreusable and cannot be shared. In this paper, a life-cycle approach to ontology-based risk management framework for software projects is presented. A dataset from literature, domain experts, and practitioners is used. The identified risks are refined by 19 software experts; risks are conceptualized, modeled, and developed using Protege. The risks are qualitatively analyzed and prioritized, and aversion methods are provided. The framework is adopted in real-life software projects. Precision recall and F-measure metrics are used to validate the performance of the extraction tool while performance and perception evaluation are carried out using the performance appraisal form and technology acceptance model, respectively. Mean scores from performance and perception evaluation are compared with evaluation concept scale. Results showed that cost is reduced, high-quality projects are delivered on time, and software developers found this framework a potent tool needed for their day-to-day activities in software development.eninfo:eu-repo/semantics/closedAccesshierarchical risk classification breakdown structure (HBRS)performance appraisal form (PAF)software development life cycle (SDLC)software ontology-based risk management (SORM)software risk ontology (SRO)technology acceptance model (TAM)Toward Ontology-Based Risk Management Framework for Software Projects: an Empirical StudyArticleQ33212WOS:00054216320000113