classified as acceptable when it experiences a suitably specified run of successes. While such methods are not yet in use, Gaver’s talk clearly demonstrated that new, different, and promising procedures for acceptance testing are feasible and under development.
Discussion of reliability growth continued in the workshop session dedicated to models, methods, and applications involving the linkage between field performance data and reliability growth. Fritz Scholz described a model applicable to the detection and removal of design flaws in a fielded system and discussed a methodology for estimating and bounding system reliability at each stage of the fault discovery process. William Meeker then presented a series of examples drawn from his experience with field data in the automotive industry—examples that motivate and strongly support the continuous tracking of performance data once an item has been fielded.
After summarizing these four presentations, we turn to the important issue of the management of reliability growth. The presentations of Jane Booker and Larry Crow were both representative of the modern global approach to reliability growth, which incorporates the best features of the classical theory yet goes well beyond it, using ideas that are integrative, inter- and cross-disciplinary, and comprehensive.
Ananda Sen provided a review of recent developments in modeling and statistical inference for reliability growth. In typical modern applications of reliability growth theory, a system’s reliability is improved through a series of test, analyze, and fix (TAAF) episodes. Reliability growth modeling is a collection of techniques that attempt to model the (hoped-for) increasing reliability of a system as a function of the time spent in development and other relevant variables. Reliability growth modeling has historically played a role in helping to determine whether a system in development is likely to meet reliability requirements in time for graduation to the next development phase, and eventually to operational testing. Sen focused his presentation on systems for which the relevant data input into reliability growth models consists of successive times to failure (that is, total test time). See Sen (1998) for more detail.
Of course, it is not clear whether the process of reliability improvement can be usefully modeled as a function of time alone, since time is an indirect measurement of the workings of the TAAF cycle. However, it is useful to attempt to do so since these models can be used to (1) monitor the