Requirement prioritization is a critical process in software engineering. It helps project teams decide what features to implement first. Traditional methods often ignore uncertainty in human judgment. To overcome this, fuzzy logic and multi-criteria decision-making (MCDM) techniques like TOPSIS have been widely adopted.
We employed Fuzzy TOPSIS to rank software requirements. Data was collected through expert surveys, and fuzzy numbers were used to represent linguistic variables. The TOPSIS process was then used to calculate the closeness coefficient for each requirement.
The results demonstrated that Fuzzy TOPSIS could effectively handle vagueness and prioritize the most important requirements with minimal bias. The technique proved useful in both academic and industry settings.
The proposed method improves requirement prioritization by accounting for uncertainty and stakeholder preference. It can be applied to large-scale software projects where decision-making complexity is high.