Learning theory: A new perspective for reliability testing evaluation

Software, hardware, and procedure reliability modeling and prediction have received significant attention by the research community over the past decades, but relatively 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, the methods are applied to several widely studied historical data sets. In several of these cases, no evidence of learning was detected, suggesting that additional faults may have eluded testers and inadvertently been included in the final product. Thus, the proposed techniques can offer additional guidance to managers concerned with the thoroughness of product and procedure testing.