Tuning of COCOMO Model Parameters by using Bee Colony Optimization

Constructive Cost Model (COCOMO) used parameters for software effort estimation, which were calculated in 1981 by regression analysis of 63 types of project data; therefore applying these parameters to current project development will not generate accurate results. The objective of current research is applying Bee Colony Optimization (BCO) metaheuristic approach to optimize the parameters of COCOMO model for improving software cost estimation. The Bee Colony Optimization (BCO) is a new branch of Swarm Intelligence and has been applied successfully to various engineering disciplines. BCO approach is a “bottom-up” approach to modeling where special kinds of artificial agents are created by analogy with bees. These artificial agents or bees are used to solve complex combinatorial optimization problems. The proposed model validation is carried out using Interactive Voice Response software project dataset of a company. The results generated by the proposed model are compared to those obtained by methods proposed in the literature using Walston-Felix, SEL, Bailey-Basil, COCOMO II and Halstead models. The BCO approach generates various partial solutions and best solution is selected based on Mean Magnitude of Relative Error. The results obtained show that the proposed BCO based model is able to improve the accuracy of cost estimation and also outperform other models.