Software Reliability Issues: An Experimental Approach

In this thesis, we present methodologies involving a data structure called the debugging graph whereby the predictive performance of software reliability models can be analyzed and improved under laboratory conditions. This procedure substitutes the averages of large sample sets for the single point samples normally used as inputs to these models and thus supports scrutiny of their performances with less random input data. Initially, we describe the construction of an extensive database of empirical reliability data which we derived by testing each partially debugged version of subject software represented by complete or partial debugging graphs. We demonstrate how these data can be used to assign relative sizes to known bugs and to simulate multiple debugging sessions. We then present the results from a series of proof-of-concept experiments. We show that controlling fault recovery order as represented by the data input to some well-known reliability models can enable them to produce more accurate predictions and can mitigate anomalous effects we attribute to manifestations of the fault interaction phenomenon. Since limited testing resources are common in the real world, we demonstrate the use of two approximation techniques, the surrogate oracle and path truncations, to render the application of our methodologies computationally feasible outside a laboratory setting. We report results which support the assertion that reliability data collected from just a partial debugging graph and subject to these approximations qualitatively agrees with those collected under ideal laboratory conditions, provided one accounts for optimistic bias introduced by the surrogate in later prediction stages. We outline an algorithmic approach for using data derived from a partial debugging graph to improve software reliability predictions, and show its complexity to be no worse than O(n2). We summarize some outstanding questions as areas for future investigations of and improvements to the software reliability prediction process