An experimental comparison of five prioritization methods - Investigating ease of use, accuracy and scalability

Abstract: Requirements prioritization is an important part of developing the right product in the right time. There are different ideas about which method is the best to use when prioritizing requirements. This thesis takes a closer look at five different methods and then put them into an controlled experiment, in order to find out which of the methods that would be the best method to use. The experiment was designed to find out which method yields the most accurate result, the method’s ability to scale up to many more requirements, what time it took to prioritize with the method, and finally how easy the method was to use. These four criteria combined will indicate which method is more suitable, i.e. be the best method, to use in prioritizing of requirements. The chosen methods are the well-known analytic hierarchy process, the computer algorithm binary search tree, and from the ideas of extreme programming come planning game. The fourth method is an old but well used method, the 100 points method. The last method is a new method, which combines planning game with the analytic hierarchy process. Analysis of the data from the experiment indicates that the planning game combined with analytic hierarchy process could be a good candidate. However, the result from the experiment clearly indicates that the binary search tree yields accurate result, is able to scale up and was the easiest method to use. For these three reasons the binary search tree clearly is the better method to use for prioritizing requirements