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Software Requirements Prioritization (SRP) is one of the crucial processes in software requirements engineering. It presents a challenging task to decide among the pool of requirements and the variance of the stakeholder’s needs in prioritizing requirements. Semi-automated requirements prioritization is implemented in both manual and automatic processes. When prioritizing requirements, these aspects such as importance, time, cost and risk, should be taken into account. The emergence of machine learning is advancing to improve and automate the SRP process whereby decision making can be performed with minimal human intervention. Incorporating machine learning approaches in prioritization techniques can be implemented in the ranking process and classifying the priority group of the software requirements. A Semi-Automated Requirements Prioritization framework (SARiP), which implements semi-automatic process in requirements prioritization is proposed. SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. SARiP has been successfully evaluated in the government sector domain by the i-Tegur team from the Department of Information Technology, Ministry of Housing and Local Government of Malaysia (KPKT). 80% of the participants agreed that SARiP is extremely likely to help the participants in prioritizing the requirements for their projects. All participants agreed that SARiP is reliable and useful. Recording the requirements and results for the prioritization will be considered for future work and traceability function will be included to trace the requirements changes.
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