Abstract: As new market opportunities, technologies, platforms, and frameworks become available, systems require large-scale and systematic architectural restructuring to accommodate them. Today’s architects have few tools and techniques to help them plan this architecture evolution. In particular, they have little assistance in planning alternative evolution paths, trading-off various aspects of the different paths, or knowing best practices for particular domains. In this paper we describe an approach for assisting architects in developing and reasoning about architectural evolution paths. The key in-sight of our approach is that, architecturally, many system evolutions follow certain common patterns – or evolution styles. We define what we mean by an evolution style, and show how it can be used to provide automated assistance for expressing architectural evolution, and for reasoning about both the correctness and quality of evolution paths.

Abstract: An emergent domain that raises some unique engineering challenges is that of software architectures to convert large bodies of unstructured data to human-usable knowledge, such as in the domain of socio-cultural information analysis. We report on an architecture based on Service-Oriented Architectures that we are applying in this domain. We list the requirements that such an architecture must support, describe our architecture for addressing them, and outline what we believe are the important engineering and research issues that must still be overcome.

Abstract: Notations and techniques for architectural modeling and analysis have matured considerably over the past two decades. However, to date these approaches have primarily focused on architectural properties and behavior that can be precisely defined. In this paper we argue that it is possible to augment existing architecture description languages (ADLs) to support reasoning and analysis in the presence of uncertainty. Specifically, we outline two basic extensions to formal architecture descriptions that take advantage of probabilistic specifications to support architecture-based analyses such as simulation, detection of behavioral drift, and reasoning about the expected outcomes of uncertain behavior. An important property of these specifications is that they allow incremental refinement � as more is known about the behavior of the system, specifications can be extended without invalidating previous analyses.

Abstract: Over the past decade there has been considerable progress in the development of formal methods to improve our confidence in complex systems. Today the use of such methods in certain fields, such as hardware design, or nuclear power control systems, is de rigueur, with commensurate improvements in quality and reliability.

Regrettably, however, the use of formalism in the medical device domain is relatively sparse. This is due in large part to the perceived difficulty of using formal methods by ordinary engineers and domain specialists, and by the lack of training in how best to apply existing tools to solve the problems faced in that domain.

Over the past few years we have been developing educational materials to help bridge this gap. Specifically we have developed a course in formal modeling for practicing engineers. A core component of this effort is a set of exercises drawn from the medical device domain, which are used to

a) show how formal modeling can be used as an effective technique to improve quality and reliability of software-intensive systems

b) provide guidelines on selecting appropriate modeling approaches for the problem at hand

c) give students hands-on experience in modeling and tool-assisted analysis

In this paper we outline our use of medical device challenge problems in achieving these goals. We argue that such exercises (and the underlying concepts) can go a long way towards bridging the gap between theory and practice, and could be used more generally to improve the state of the practice in developing high-confidence systems, in general, and medical devices, in particular.