Applying Particle Swarm Optimization to estimate software effort by multiple factors software project clustering

In the IT industry, precisely evaluate the effort of each software development project to develop cost and development schedule management to the software company in the software are count for much. Since a project, majority of development teams will feel time isn't enough to use or the project valuation be false to make the software project failed. However the cost of the software project is almost a manpower cost, manpower cost and then become a direct proportion with development schedule, so precise effort the valuation more seem to be getting more important. Consequently, this research will use Pearson product-moment correlation coefficient and one-way analyze to select several factors then used K-Means clustering algorithm to software project clustering. After project clustering, we use Particle Swarm Optimization that take mean of MRE (MMRE) as a fitness value and N-1 test method to optimization of COCOMO parameters. Finally, take parameters that finsh the optimization to calculate the software project effort that is want to estimation. This research use 63 history software projects data of COCOMO to test. The experiment really expresses using base on project clustering with multiple factors can make more effective base on effort of the estimate software of COCOMO's three project mode.