MACHINE LEARNING TECHNIQUES IN SOFTWARE EFFORT ESTIMATION USING COCOMO DATASET - Bhatia, Sonam; Attri, Varinder Kaur (IJRDO -Journal of Computer Science Engineering, 2015)
One of the most important tasks in software planning and management is estimation of the effort .Software hasplayed an crucial model in software engineering and development for , complex systems. Reliable estimating thesoftware size, cost, effort and schedule greatest challenge for software developers today. Overestimates andunderestimates have direct impact for causing damage to software companies. In this paper we introduce a methodbased on machine learning technique. Linear regression and Multiperceptron are the most popular machinetechniques for software development effort estimation. In this paper linear regression and multiperceptron havebeen used to predict the early stage effort estimations using the COCOMO dataset. It has been found thatmultiperceptron is able to predict the early stage efforts more efficiently in comparison to the linear regressionmodels