Publication Type : Conference Proceedings
Publisher : Springer Singapore
Source : Smart Innovation, Systems and Technologies
Url : https://doi.org/10.1007/978-981-16-3675-2_32
Campus : Kochi
School : School of Computing
Year : 2021
Abstract : The cost and effort for developing software projects gain a growing interest in recent years. Defining these parameters is considered as a valuable goal in achieving efficiency in developing the projects. Implementing the COCOMO model in effort estimation helps the project developers to allocate the resources efficiently. But there lies a main problem to optimize the constants in the COCOMO model. In this study, we present a way to optimize these constants using genetic algorithm by comparing two different methods in calculating the fitness function. Identifying the efficient method among them will help the optimization of COCOMO parameters and makes the effort estimation more efficient.
Cite this Research Publication : K. P. Mohamed Shabeer, S. I. Unni Krishnan, G. Deepa, Software Effort Estimation Using Genetic Algorithms with the Variance-Accounted-For (VAF) and the Manhattan Distance, Smart Innovation, Systems and Technologies, Springer Singapore, 2021, https://doi.org/10.1007/978-981-16-3675-2_32