Optimization of COCOMO II Effort Estimation using Genetic Algorithm
Astha Dhiman, Chander Diwaker · 2013
Software effort estimation is one of the essential steps to be carried out in the project planning. The effective and efficient development of the software requires accurate estimates. Software researchers are providing many cost estimation methods for several decades. Among those methods, COCOMO II is the most commonly used model because of its simplicity for estimating the effort in person-month for a project at the different stages. Today's effort estimation models are based on soft computing techniques as neural network, genetic algorithm, the fuzzy logic modeling etc. for finding the accurate predictive software development effort and time estimation. As there is no clear guideline for designing neural networks approach and also fuzzy approach is hard to use. Genetic Algorithm can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation. This work aims to propose a genetic algorithm for optimizing current coefficients of COCOMO II model to achieve more accuracy in estimation of software development effort.