MACHINE LEARNING TECHNIQUES IN SOFTWARE EFFORT ESTIMATION USING COCOMO DATASET
Abstract
One of the most important tasks in software planning and management is estimation of the effort .Software has
played an crucial model in software engineering and development for , complex systems. Reliable estimating the
software size, cost, effort and schedule greatest challenge for software developers today. Overestimates and
underestimates have direct impact for causing damage to software companies. In this paper we introduce a method
based on machine learning technique. Linear regression and Multiperceptron are the most popular machine
techniques for software development effort estimation. In this paper linear regression and multiperceptron have
been used to predict the early stage effort estimations using the COCOMO dataset. It has been found that
multiperceptron is able to predict the early stage efforts more efficiently in comparison to the linear regression
models
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