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2018 | OriginalPaper | Chapter

Efficient Tuning of COCOMO Model Cost Drivers Through Generalized Reduced Gradient (GRG) Nonlinear Optimization with Best-Fit Analysis

Authors : Surjeet Dalal, Neeraj Dahiya, Vivek Jaglan

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

The software effort estimation phase is especially critical in the software development phase. This phase is principally oriented on manipulation of the values of the cost drivers and scale factors. Also, most of the models depend on the size of the project, and a diminutive alteration in the size directs to the in proportion alterations in the effort. Miscalculations of the cost drivers have even additional ear-splitting data as a result too. In this paper, the approach of generalized reduced gradient (GRG) nonlinear optimization with best-fit analysis has been applied to tune the COCOMO model cost drivers so that level of accuracy can be achieved. This proposed methodology has been observed more efficiently in providing the software effort estimation through the help of minimizing MRE value. We have applied this methodology on NASA 63 data sets. We have shown the comparison between the estimated MRE and actual MRE of the data sets. We have also exposed the evaluation between the estimated MMRE and actual MMRE.

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Metadata
Title
Efficient Tuning of COCOMO Model Cost Drivers Through Generalized Reduced Gradient (GRG) Nonlinear Optimization with Best-Fit Analysis
Authors
Surjeet Dalal
Neeraj Dahiya
Vivek Jaglan
Copyright Year
2018
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-10-6871-3

Electronic ISBN: 978-981-10-6872-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-6872-0_32