AADLib, A Library of Reusable AADL Models

The SAE Architecture Analysis and Design Language is now a well-established language for the description of critical embedded systems, but also cyber-physical ones. A wide range of analysis tools is already available, either as part of the OSATE tool chain, or separate ones. A key missing elements of AADL is a set of reusable building blocks to help learning AADL concepts, but also experiment already existing tool chains on validated real-life examples. In this paper, we present AADLib, a library of reusable model elements. AADLib is build on two pillars: 1/ a set of ready-to- use examples so that practitioners can learn more about the AADL language itself, but also experiment with existing tools. Each example comes with a full description of available analysis and expected results. This helps reducing the learning curve of the language. 2/ a set of reusable model elements that cover typical building blocks of critical systems: processors, networks, devices with a high level of fidelity so that the cost to start a new project is reduced. AADLib is distributed under a Free/Open Source License to further disseminate the AADL language. As such, AADLib provides a convenient way to discover AADL concepts and tool chains, and learn about its features

Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Tools for Real-Time Control Systems Co-Design : A Survey

This report presents a survey of current simulation tools in the area of integrated control and real-time systems design. Each tool is presented with a quick overview followed by a more detailed section describing comparative aspects of the tool. These aspects describe the context and purpose of the tool (scenarios, development stages, activities, and qualities/constraints being addressed) and the actual tool technology (tool architecture, inputs, outputs, modeling content, extensibility and availability). The tools presented in the survey are the following; Jitterbug and TrueTime from the Department of Automatic Control at Lund University, Sweden, AIDA and XILO from the Department of Machine Design at the Royal Institute of Technology, Sweden, Ptolemy II from the Department of Electrical Engineering and Computer Sciences at Berkeley, California, RTSIM from the RETIS Laboratory, Pisa, Italy, and Syndex and Orccad from INRIA, France. The survey also briefly describes some existing commercial tools related to the area of real-time control systems

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

Compilation of Heterogeneous Models: Motivations and Challenges

International audienceThe widespread use of model driven engineering in the development of software-intensive systems, including high-integrity embedded systems, gave rise to a "Tower of Babel" of modeling languages. System architects may use languages such as OMG SysML and MARTE, SAE AADL or EAST-ADL; control and command engineers tend to use graphical tools such as MathWorks Simulink/Stateflow or Esterel Technologies SCADE, or textual languages such as MathWorks Embedded Matlab; software engineers usually rely on OMG UML; and, of course, many in-house domain specific languages are equally used at any step of the development process. This heterogeneity of modeling formalisms raises several questions on the verification and code generation for systems described using heterogeneous models: How can we ensure consistency across multiple modeling views? How can we generate code, which is optimized with respect to multiple modeling views? How can we ensure model-level verification is consistent with the run-time behavior of the generated executable application?In this position paper we describe the motivations and challenges of analysis and code generation from heterogeneous models when intra-view consistency, optimization and safety are major concerns. We will then introduce Project P 2 and Hi-MoCo 3-respectively FUI and Eurostars-funded collaborative projects tackling the challenges above. This work continues and extends, in a wider context, the work carried out by the Gene-Auto 4 project [1], [2]. Hereby we will present the key elements of Project P and Hi-MoCo, in particular: (i) the philosophy for the identification of safe and minimal practical subsets of input modeling languages; (ii) the overall architecture of the toolsets, the supported analysis techniques and the target languages for code generation; and finally, (iii) the approach to cross-domain qualification for an open-source, community-driven toolset