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Research Article Open Access
Software cost estimation is the very basic step to start any project. It provides us the overview of effort, resources and time required for a project in terms of cost.Project successes mostly depend on Cost estimation as it gives the initial idea of the path, challenges and risk involved in the project. It is very difficult to match approximately the estimated with the actual cost. Efficient estimate can help us make more reliable decisions in planning the project risk. Cost estimation has become very important as it may lead to adverse results if the predicted estimates are wrong. In this model, we have proposed a value shrinking technique based on multilayer feed-forward neural network, and auto-associative clustering. The Kernel component analysis is log-linear regression functions calibrated with large data set with ordinary least squares. We have showed that Kernel component analysis can improve the estimation model accuracy by shrinking the input variables into an equivalent pattern and removing irrelevant variable, based on the COCOMOII data set. We have showed that the models obtained by applying Kernel component analysis are more persistent, acceptable and dependable.