Software project schedule variance prediction using Bayesian Network

The major objective of software engineer is to guarantee to deliver high-quality software on time and within budget. But as the development of software technology and the rapid extension of application areas, the size and complexity of software is increasing so quickly that the cost and schedule is often out of control. However, few groups or researchers have proposed an effective method to help project manager make reasonable project plan, resource allocation and improvement actions. This paper proposes Bayesian Network to solve the problem of predicting and controlling of software schedule in order to achieve proactive management. Firstly, we choose factors influencing software schedule and determine some significant cause-effect relationship between factors. Then we analyze data using statistical analysis and deal with data using data discretization. Thirdly, we construct the Bayesian structure of software schedule and learn the condition probability table of the structure. The structure and condition probability table constitute the model for software schedule variance. The model can be used not only to help project manager predict probability of software schedule variance but also guide software developers to make reasonable improvement actions. At last, an application shows how to use the model and the result proves the validity of the model. In addition, a sensitivity analysis is developed with the model to locate the most important factor of software schedule variance.