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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 337))

Abstract

Management of large software projects includes estimating software development effort as the software industry is unable to provide a proper estimate of effort, time and development cost. Though many estimation models exist for effort prediction, a novel model is required to obtain highly accurate estimations. This paper proposes a Generalized Regression Neural Network to utilize improved software effort estimation for COCOMO dataset. In this paper, the Mean Magnitude Relative Error (MMRE) and Median Magnitude Relative Error (MdMRE) are used as the evaluation criteria. The proposed Generalized Regression Neural Network is compared with various techniques such as M5, Linear regression, SMO Polykernel and RBF kernel.

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Correspondence to Parasana Sankara Rao .

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Rao, P.S., Kumar, R.K. (2015). Software Effort Estimation through a Generalized Regression Neural Network. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-13728-5_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13727-8

  • Online ISBN: 978-3-319-13728-5

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