An Improved Algorithmic Method for Software Development Effort Estimation

Abstract

Accurate estimating is one of the most important activities in the field of software project management. Different aspects of software projects must be estimated among which time and effort are of significant importance to efficient project planning. Due to complexity of software projects and lack of information at the early stages of project, reliable effort estimation is a challenging issue. In this paper, a hybrid model is proposed to estimate the effort of software projects. The proposed model is a combination of particle swarm optimization algorithm and a linear regression method in which coefficient finding is optimally performed. Moreover, the estimation equation is adjusted using project size metric so that the most accurate estimate is achieved. A relatively real large data set is employed to evaluate the performance of the proposed model and the results are compared with other models. The obtained results showed that the proposed hybrid model can improve the accuracy of estimates.