Optimization of COCOMO II model to Estimate software cost using Squirrel Algorithm

Abstract

Estimating software effort important highly role of project management which enables Estimate software cost. Inexact results may reason to increment or decrement in estimating effort. A lot of technique concerning software effort prediction was has applied for enhancing models that build optimal prediction accuracy. One of these techniques is Swarm intelligence. The process of choosing an optimal prediction algorithm depends on experts and complexity. The proposed paper enhances the prediction hire the COCOMO idea by executing the Squirrel Search Algorithm(SSA). The search behavior in squirrels is dependent on dynamic jumping and gliding techniques by applying the algorithm on four data sets (NASA 93, NASA 60, NASA 63, Bailey and Basili 18) and assess with the use of Mean Magnitude of Relative Error(MMRE). The score shows that the Squirrel search model has obtained best the coefficient values of the COCOMO model than Algorithms applied in this field.