The aim of this study is to provide an efficient methodology in estimating project development cost using analogy. Cost estimation is one of the greatest challenges in software industry to be successful enough in delivering a project within the schedule and with quality. In most cases, the delivered product either loses out on the quality or the expected timeline, owing to improper and imprecise estimation of the project cost. Deriving near accurate project cost could be done using analogy, wherein previous project data set is manipulated to arrive at the accurate cost for the current project. Ant Colony Optimization (ACO) technique implemented over analogy provides a better solution to overcome the challenges faced during cost estimation. In our study, we follow a three step methodology, based on ACO to arrive at the project development cost from promised datasets. Firstly, we extract the matching projects from data set based on the nearest values of a parameter. Secondly, we identify and group projects based on sizing. Thirdly, we improve similarity measures to match our project against those in data set.
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