Predictability measures for software reliability models

Abstract

A two-component predictability measure is presented that characterizes the long-term predictability of a software reliability growth 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. The results seem to support the observation that the logarithmic model appears to have good predictability is most cases. However, at very low fault densities, the exponential model may be slightly better. The delayed S-shaped model which in some cases has been shown to have good fit, generally performed poorly.<>