Albert Benveniste

Recently, contract-based design has been proposed as an orthogonal approach that complements system design methodologies proposed so far to cope with the complexity of system design. Contract-based design provides a rigorous scaffolding for verification, analysis, abstraction/refinement, and even synthesis. Several results have been obtained in this domain but a unified treatment of the topic that can help put contract-based design in perspective has been missing. Contracts for System Design provides such a treatment where contracts are precisely defined and characterized so that they can be used in design methodologies with no ambiguity. It identifies the essence of complex system design using contracts through a mathematical meta-theory, where all the properties of the methodology are derived from a very abstract and generic notion of contract. This meta-theory provides deep and illuminating links with existing contract and interface theories, as well as guidelines for designing new theories. It encompasses contracts for both software and systems, with emphasis on the latter. Contracts for System Design illustrates the use of contracts with two examples: requirement engineering for a parking garage management, and the development of contracts for timing and scheduling in the context of the AUTOSAR methodology in use in the automotive sector.

Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.