A regression analysis of a Space Shuttle software failure data set

Abstract

Regression methods are employed in analyzing a Space Shuttle failure data set. A class of models based on transforms of cumulative time and cumulative failures is considered. It is used to predict both current as well as future log transformed failure intensity. This class includes several popular software reliability models including the exponential, logarithmic, and power models. It also includes models based on transforms of the time per failure, the time-varying analogue to the mean time between failures. Models are compared on the basis of their predictive performance as measured by the predicted residual sum of squares (PRESS) criterion. Time per failure is included as one of the independent variables in the models identified as having the lowest PRESS score, both when predicting current as well as future log failure intensity. Analyses are conducted using failures within a final subset of the observation interval. This subset is chosen through inspection of the plot of time per failure in terms of cumulative time. The impact of the choice of this subset is assessed by comparing results for analyses of that subset with analyses of the data in the complete observation interval.