Abstract
The existing software Risk Assessment Model uses nine Critical Risk Elements (CRE) in its risk assessment. As the complexity of the software increases, the existing model becomes obsolete and experiences some limitations in assessing risk efficiently. In this paper, an optimized software risk assessment model with twelve critical risk elements was developed using genetic algorithm to efficiently manage risk elements. All simulations were performed in Matlab. Quantitative research methodology was deployed for data collections and results obtained show that the model with twelve critical risk elements optimally manages and assesses risk than the one with just nine CRE.
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