Designing Software Architectures As a Composition of Specializations of Knowledge Domains

This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience

Multi-Decision Policy and Policy Combinator Specifications

Margrave is a specification language and analysis tool for access control policies with semantics based in order-sorted logic. The clear logical roots of Margrave\u27s semantics makes policies specified in the Margrave language both machine analyzable and relatively easy for users to reason about. However, the decision conflict resolution declaration and policy set features of Margrave do not have semantics that are as cleanly rooted in order-sorted logic as Margrave policies and queries are. Additionally, the current semantics of decision conflict resolution declarations and of policy sets do not permit users to take full advantage of the multi-decision capabilities of Margrave policies. The purposes of this thesis are (i) to provide a unified extension to the semantics for policies and policy combination, (ii) to cleanly support decision conflict resolution mechanisms in a general way within those semantics and (iii) to provide insight into the properties of policy combination and decision conflict resolution for multi-decision policies in general. These goals are achieved via the realization that policy combinators may be treated as policies operating within environments extended with the results of the policies to be combined, allowing policy combinators to be defined as if they were policies. The ability to treat policy combinators as policies means that users\u27 current understanding of policies can be easily extended to policy combinators. Additionally, the tools that Margrave has for supporting policies can be leveraged as the Margrave language and analysis tool grow to provide fuller support for policy combination and rule conflict resolution declarations