Research On Theory And Models Of Software Project Risk Management

Risk is ubiquitous in the process of investment or development and implementation of software project. Risk management becomes more important with the increase of software complexity and requirement. It is an urgent problem to study the strategies to analyze and control risk.The main contents of this paper are as follows: Firstly, the history and state-of-the-art of the risk management are reviewed both domestically and abroad. Then, the concepts about risk management and software risk management are defined based on the concept of risk. The theoretical foundation of risk management and the particularity of software project risk management are discussed in detail. After four styles of risk management systems are analyzed, their advantages and disadvantages are compared. Thirdly, based on establishing a risk evaluation system of software projects, a risk evaluation model of software project investment based on Artificial Neural Network (ANN) is proposed. The model is capable to evaluate risks effectively in the process of software project investment by verifying the data of software projects. And in the process of modeling, the factor analysis is utilized to decrease dimension of sample data and an optimization algorithm based on the principle of golden section is designed to find the optimal number of hidden layer nodes. Fourthly, a dynamic risk management model of software project development is proposed. The model can monitor risk states continually by using risk track module and then adjust risk list according to the change of risk states so that the activities of risk management run in the flow with feedback. Fifthly, A risk analysis implementation flow based on Bayesian Networks(BNs) for risk analysis module is presented. In addition, the BNs is applied to risk analysis module of a real case. The method can update risk data and change the states of risk nodes online. It is fundamental to implement dynamic risk management in the process of software development. In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly. At last, after summarizing main content discussed in this paper, potential research directions are pointed out.