The models deals efficiently with imprecise and uncertain input and enhances the reliability of software effort estimation. In this study we proposed two models using particle swarm optimization (PSO) with Constriction Factor for fine tuning of parameters of the constructive cost model (COCOMO) effort estimation. So far many models are proposed by using machine learning algorithms, but no model is proved successful for efficiently and consistently predicting the effort. Software effort estimation is the most important activity in project planning. The basic goal of project planning is to look into the future, identify the activities that need to be done to complete the project successfully and plan scheduling and resource allocation for these activities.
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