A Risk Control Optimization Model for Software Project

Risk control is of great importance for software project management. However, the software risk control currently depends on the manager's subjective judgments, and lacks quantitative supporting tools. In this paper a software risk control optimization model is presented. By forming project strategy and increasing activities' costs, the objective of maximizing the expected value of project's capital investment is achieved. Moreover, because the model is a NLP, a particle swarm algorithm approach is proposed. Finally an example is utilized to verify the effectiveness of this method. The research provides strong supports for effectively managing and quantitatively controlling software risk, control activities And reduce the probability of failure project, maximize the project expected revenue.