ACCURATE SOFTWARE RELIABILITY ESTIMATION Submitted by Jason

OF THESIS ACCURATE SOFTWARE RELIABILITY ESTIMATION A large number of software reliability growth models are now available. It is widely known that none of these models performs well in all situations, and that choosing the appropriate model a priori is difficult. For this reason recent work has focused on how these models can be made more accurate, rather than trying to find a model which works in all cases. This includes various efforts at data filtering and recalibration, and an examination of the physical interpretation of model parameters. Here we examine the impact of the parameter estimation technique on model accuracy, and show that the maximum likelihood method provides for estimates which are more reliable than the least squares method. We present an interpretation of the parameters for the popular logarithmic model, and show that it may be possible to use this interpretation to overcome some of the difficulties found in working with early failure test data. We present a new software reliability model, based on the objective measure of program coverage, and show how it can be used to predict the number of defects in a program. We discuss the meaning of the parameters of this model, and suggest what needs to be done in order to gain a greater understanding of it. Finally, we present a tool we have developed which supports and integrates many of the techniques and methods presented here, making them easily accessible to practitioners. Jason Allen Denton Computer Science Colorado State University Fort Collins, Colorado 80523 Fall 1999