Date of Award

Summer 8-1996

Document Type


Degree Name

Master of Science (MS)


Computer Science

Committee Director

Larry Wilson

Committee Member

Michael Dovial

Committee Member

J. Christian Wild

Committee Member

Steven J. Zeil

Call Number for Print

Special Collections LD4331.C65 B68


Many software reliability models have been promoted in the literature in an attempt to provide a meaningful response to the growing need to know the reliability of software during and after the development process. However, there are well-founded concerns as to the accuracy of the individual model, as well as to the choice of which model to use for a particular program. A recent experiment based on the debugging history of a partially debugged program used a gold version to label a debugging graph from replicated data. This experiment concluded that presenting the replicated data to the models in a way that approximated a debugging session with the largest bugs found first allowed the Logarithmic Poisson model to make highly accurate predictions for the program in question. The experiment also showed that in the absence of a gold program a surrogate can be created by using the program with all known fixes installed. The contribution of this thesis is fourfold. First, the results of the previous experiment are corroborated on a different program written to the same specifications. Second, an approximation technique which uses the deltas or differences between two versions of the program to approximate the reliability change between those versions is shown to be a viable alternative to the gold or surrogate gold techniques. Third, the data variation due to the random input generation process is investigated and shown to not have a significant effect on the results of these experiments. Fourth, implementation schemes are presented for using the surrogate gold and delta techniques in a software development environment and are shown to produce accurate predictions for the program in this experiment.


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