Predictability measures for software reliability models

It is critical to be able to achieve an acceptable quality level before a software package is released. It is often important to meet a target release date. To be able to estimate the testing efforts required, it is necessary to use a software reliability growth model. While several different software reliability growth models have been proposed, there exist no clear guidelines about which model should be used. Here a twwcomponent predictability measure is presented that characterizes the long term predictability of a model. The first component, average predictability, measures how well a model predicts throughout the testing phase. The second component, average bias, is a measure of the general tendency to overestimate or underestimate the number of faults. Data sets for both large and small projects from diverse sources have been analyzed. Results presented here indicate that some models perform better than others in most cases.