Abstract
Software, hardware, and procedure reliability modeling and prediction have enjoyed ample attention by the research community over the past decades. However, significantly less research has been dedicated to studying the contribution of the human element of reliability engineering. This paper applies several mathematical models from the theory of learning in search of evidence for learning trends during the testing phase. To demonstrate the utility of the approach, learning models such as the Stevens-Savin equation are applied to several widely studied datasets from the historical literature. In several cases, no evidence of learning was detected, which suggests that additional faults may have eluded testers and been included in the final product. We also discuss how models that are based on learning theory can be used to predict the number of failures remaining. The results illustrate that learning models can predict the number of failures remaining in software more accurately than traditional software reliability growth models that were based on the nonhomogeneous Poisson process. Thus, the proposed techniques produce predictions like existing reliability models, but also offer additional guidance to managers concerned with the thoroughness of product and procedure testing.