9803879 There is a substantial body of literature devoted to directed testing methods, which manipulate the choice of test inputs so as to increase the probability and/or rate of fault detection. These include most well-known testing methods, including functional and structural testing. A recurring problem with these methods has been determining when to stop testing. In contrast, a variety of reliability growth models provide quantified measures of test effectiveness in terms that are directly relevant to project management, but at the cost of restricting testing to representative selection, in which test data is chosen to reflect the operational distribution of the program's inputs. This research seeks a common ground between the areas of directed testing and reliability modeling, allowing statistical management of software testing without severely restricting the choice of test method. It explores reliability models that can be applied to non-representative test processes and improved data collection processes for reliability modeling. Specific avenues of investigation include: 1) an order statistic model of reliability growth based upon fault failure rates, 2) the potential for employing fault failure rate information with existing reliability growth models, and 3) the relative efficacy of different techniques for measuring fault failure rates.***
