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Software effort estimation is the process of predicting the most realistic use of effort required to develop or maintain software based on incomplete, uncertain and/or noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets etc. In this paper, the performance of single conjunctive rule learner, M5-Rules Algorithm and decision table majority classifier is compared for Modeling of Effort Estimation of Software Projects. The performance of the developed models were tested on 93 NASA projects from different centers for projects from 1971-1987.
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