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Software Effort estimation is a process of predicting the amount of effort to develop software projects. The agile methodologies adopted on an idea of iterative and evolutionary development processes. Also, the requirements of agile projects subjected to change during the development phase. So, the using of the traditional effort estimation techniques can produce inaccurate estimation. Furthermore, there are many factors that affect the process of measuring effort in the agile projects; velocity factors, story size factors, complexity factors, and, implementation level factors. Therefore, the authors propose an algorithmic model based on fuzzy logic approach and agile factors to increase the accuracy of agile project effort estimation. The proposed model is formulated to idealize the COCOMO II and the agile estimation factors. The results are evaluated via Magnitude of Relative Error and Prediction Level metrics. The using of the fuzzy logic approach will improve the effort estimation accuracy by characterizing the agile estimation factors using the trapezoidal membership function.
