Systems cannot be deployed until customer organizations judge them fit for use in the mission
environment. These assurance judgments must be based on evidence that a system manifests the
necessary functionality and does so at a level of quality and security appropriate to the operating
environment. Achieving this goal has two benefits. The first is direct: Cost-effective and rapid
recertification is essential to support the development of systems that must adapt to changes in both the
mission environment and the infrastructure environment. The second benefit is indirect: The
prerequisites for progress towards this goal are the same as those that will, in the more general case,
improve the level of assurance and the efficacy and efficiency of the processes through which it is
Assurance is a human judgment of fitness for use. This judgment is thus contextualized by the mission
definition, the features of the normative operating environment, the threat landscape, and characteristics
of the evolving infrastructure environment. The judgment must be based on evidence, and there are many
different kinds of evidence that can be produced and managed in a large-scale systems engineering
activity. The judgment must also address diverse quality attributes and ilities.
The project takes a multi-faceted approach, focusing on combines technical analysis of system artifacts
and requirements with architecture techniques to promote assurance and resiliency. An important goal
is to apply these techniques not just in anomaly detection, but also to support stronger possibilities for
positive assurance, guaranteeing an absence of defects of certain specific kinds, for greater scalability to
large systems, building on technical approaches to composability, and for more rapid execution, building
on design experience. These have been successfully applied in such areas as the High Level Architecture
analysis for networked DoD models and simulations, cyber-physical robotic systems, and extremely large
commercial Java programs.
An important goal is to develop incrementally compostable combinations of models, practices, and tools
for obtaining the most cost- and schedule-effective combinations for the assurance of necessary system
properties. One analogy to exploit is with successful techniques in other domains, such as building codes.
Building codes provide engineering guidance and constraint. But they are also continuously evolving, and
successfully doing so under the influence of diverse and conflicting stakeholder interests. Another analogy
to exploit is the idea of chains of evidence -- semantic dependency modeling -- to support ongoing reevaluation
for rapidly evolving systems both in development and in sustainment/modernization. A third
analogy is with the use of metaphors in language -- how, in the engineering and evaluation of systems, do
we choose to express the key design concepts and engineering abstractions? And how do these choices
influence the kinds of models and analytics and the extent to which assurance judgments can be reached
and at what scale of system and complexity? The abstractions come in many forms, including for example
language extensions for assurance assertions or context metadata.