PRODUCTIVITY PREDICTION MODEL BASED ON BAYESIAN ANALYSIS AND PRODUCTIVITY CONSOLE A Dissertation by SEOK

Productivity Prediction Model Based on Bayesian Analysis and Productivity Console. (May 2005) Seok Jun Yun, B.S., Korea Military Academy; M.S., Naval Postgraduate School Co–Chairs of Advisory Committee: Dr. Dick B. Simmons Dr. William M. Lively Software project management is one of the most critical activities in modern software development projects. Without realistic and objective management, the software development process cannot be managed in an effective way. There are three general problems in project management: effort estimation is not accurate, actual status is difficult to understand, and projects are often geographically dispersed. Estimating software development effort is one of the most challenging problems in project management. Various attempts have been made to solve the problem; so far, however, it remains a complex problem. The error rate of a renowned effort estimation model can be higher than 30% of the actual productivity. Therefore, inaccurate estimation results in poor planning and defies effective control of time and budgets in project management. In this research, we have built a productivity prediction model which uses productivity data from an ongoing project to reevaluate the initial productivity estimate and provides managers a better productivity estimate for project management. The actual status of the software project is not easy to understand due to problems inherent in software project attributes. The project attributes are dispersed across the various CASE (Computer-Aided Software Engineering) tools and are difficult to measure because they are not hard material like building blocks. In this research, we have created a productivity console which incorporates an expert