Mathematical modeling to reduce the cost of complex system testing: Characterizing test coverage to assess and improve information return

Abstract

Effective, cost-efficient testing is critical to the long-term success of Open Architecture within the Navy's Integrated Warfare System. In previous research we have developed a simple, effective framework to examine the testing of complex systems. This model and its prototype decision aid provide a rigorous yet tractable approach to improve system testing, and to better understand and document the system and component interdependencies across the enterprise. An integral part of this model is characterizing test coverages on modules. Using idealized simulations of complex systems, we investigate the sensitivity of test selection strategy to the precision with which these coverages are specified. Monte Carlo analysis indicates that best-test selection strategies are somewhat sensitive to the precision of test coverage specification, suggesting significant impact on testing under fixed-cost constraint. These results provide significant insight as we extend this work with further study of real-world systems by applying, and refining, the mathematical analysis and computer simulation within this framework. The current decision-aid software will be further developed using these operational test and evaluation data, improving the fidelity of the current modeling while making available to program managers and system designers a usable and relevant tool for test retest decisions.