March 16th, 2017

Today, the development costs of high confidence systems explode with their size. We are far away from the solution of the so-called, software crisis. In fact, the latter hides another much bigger: the system crisis. n my talk I will discuss rigorous system design as a formal and accountable process leading from requirements to correct-by-construction implementations. I will also discuss current limitations of the state of the art and advocate a coherent scientific foundation for system design based on four principles: 1) separation of concerns; 2) component-based construction; 3) semantic coherency; 4) correctness-by-construction. The combined application of these principles allows the definition of a methodology clearly identifying where human intervention and ingenuity are needed to resolve design choices, as well as activities that can be supported by tools to automate tedious and error-prone tasks. The presented view for rigorous system design has been amply implemented in the BIP (Behavior, Interaction, Priority) component framework and substantiated by numerous experimental results showing both its relevance and feasibility. I will conclude with a discussion advocating a system-centric vision for computing, and a deeper interaction and cross-fertilization with other more mature scientific disciplines

Testing System Intelligence

We discuss the adequacy of tests for intelligent systems and practical problems raised by their implementation. We propose the replacement test as the ability of a system to replace successfully another system performing a task in a given context. We show how it can characterize salient aspects of human intelligence that cannot be taken into account by the Turing test. We argue that building intelligent systems passing the replacement test involves a series of technical problems that are outside the scope of current AI. We present a framework for implementing the proposed test and validating the properties of the intelligent systems. We discuss the inherent limitations of intelligent system validation and advocate new theoretical foundations for extending existing rigorous test methods. We suggest that the replacement test, based on the complementarity of skills between human and machine, can lead to a multitude of intelligence concepts reflecting the ability to combine data-based and symbolic knowledge to varying degrees

Architecture Diagrams: A Graphical Language for Architecture Style Specification

Architecture styles characterise families of architectures sharing common characteristics. We have recently proposed configuration logics for architecture style specification. In this paper, we study a graphical notation to enhance readability and easiness of expression. We study simple architecture diagrams and a more expressive extension, interval architecture diagrams. For each type of diagrams, we present its semantics, a set of necessary and sufficient consistency conditions and a method that allows to characterise compositionally the specified architectures. We provide several examples illustrating the application of the results. We also present a polynomial-time algorithm for checking that a given architecture conforms to the architecture style specified by a diagram.Comment: In Proceedings ICE 2016, arXiv:1608.0313

Challenges and Work Directions for Europe

International audienceEmbedded Systems are components integrating software and hardware, that are jointly and specifically designed to provide a given set of functionalities. These components may be used in a huge variety of applications, including transport (avionics, space, automotive, trains), electrical and electronic appliances (cameras, toys, television, washers, dryers, audio systems, and cellular phones), process control (energy production and distribution, factory automation), telecommunications (satellites, mobile phones and telecom networks), security (e-commerce, smart cards), etc. We expect that within a short timeframe, embedded systems will be a part of virtually all equipment designed or manufactured in Europe, the USA, and Asia

Rigorous System Design

The monograph advocates rigorous system design as a coherent and accountable model-based process leading from requirements to correct implementations. It presents the current state of the art in system design, discusses its limitations, and identifies possible avenues for overcoming them. A rigorous system design flow is defined as a formal accountable and iterative process composed of steps, and based on four principles: (1) separation of concerns; (2) component-based construction; (3) semantic coherency; and (4) correctness-by-construction. The combined application of these principles allows the definition of a methodology clearly identifying where human intervention and ingenuity are needed to resolve design choices, as well as activities that can be supported by tools to automate tedious and error-prone tasks. An implementable system model is progressively derived by source-to-source automated transformations in a single host component-based language rooted in well-defined semantics. Using a single modeling language throughout the design flow enforces semantic coherency. Correct-by-construction techniques allow well-known limitations of a posteriori verification to be overcome and ensure accountability. It is possible to explain, at each design step, which among the requirements are satisfied and which may not be satisfied. The presented view for rigorous system design has been amply implemented in the BIP (Behavior, Interaction, Priority) component framework and substantiated by numerous experimental results showing both its relevance and feasibility. The monograph concludes with a discussion advocating a system-centric vision for computing, identifying possible links with other disciplines, and emphasizing centrality of system design

Autonomics: In Search of a Foundation for Next Generation Autonomous Systems

The potential benefits of autonomous systems have been driving intensive development of such systems, and of supporting tools and methodologies. However, there are still major issues to be dealt with before such development becomes commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next generation autonomous systems is a must. We discuss what is needed for such a foundation, identify a central aspect thereof, namely, decision-making, and focus on three main challenges: (i) how to specify autonomous system behavior and the associated decisions in the face of unpredictability of future events and conditions and the inadequacy of current languages for describing these; (ii) how to carry out faithful simulation and analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems,; and (iii) how to engineer systems that combine executable model-driven techniques and data-driven machine learning techniques. We argue that autonomics, i.e., the study of unique challenges presented by next generation autonomous systems, and research towards resolving them, can introduce substantial contributions and innovations in system engineering and computer science