Optimizing COCOMO II parameters using particle swarm method

The estimation of software effort is an essential and crucial activity for the software development life cycle. It is a problem that often appears on the project of making a software. A poor estimate will result in a worse project management. Several software cost estimation models have been introduced to resolve this problem. Constructive Cost Model II (COCOMO II Model) is a most considerable and broadly used model in cost estimation. To estimate the cost of a software project, COCOMO II model uses cost drivers, scale factors and line of code. However, the model is still lacking in terms of accuracy. In this study, we investigate the influence of components and attributes to achieve new better accuracy improvement on COCOMO II model. We introduced the use of Particle Swarm Optimization (PSO) algorithm in optimizing the COCOMO II model parameters. The proposed method is applied on Turkish Software Industry dataset. The method achieves well result and deals proficient with inexplicit data input and further improve a reliability of the estimation method. The optimized MMRE result is 34.1939%. It can reduce 698.9461% and 104.876% errors from the basic COCOMO II model and Tabu Search coefficient significantly.