Work-in-progress Assume-guarantee reasoning with ioco

This paper presents a combination between the assume-guarantee paradigm and the testing relation ioco. The assume-guarantee paradigm is a ”divide and conquer” technique that decomposes the verification of a system into smaller tasks that involve the verification of its components. The principal aspect of assume-guarantee reasoning is to consider each component separately, while taking into account assumptions about the context of the component. The testing relation ioco is a formal conformance relation for model-based testing that works on labeled transition systems. Our main result shows that, with certain restrictions, assume-guarantee reasoning can be applied in the context of ioco. This enables testing ioco-conformance of a system by testing its components separately

Self-Test Components for Highly Reconfigurable Systems

Abstract Verification of component-based systems presents new challenges not yet completely addressed by existing testing techniques. This paper proposes a new approach for automatically testing highly reconfigurable component-based systems, i.e., systems that can be obtained by changing some components. The paper presents an industrial case that motivates our research and proposes a testing infrastructure that tracks run-time information for components. The collected information is used for automatic testing new versions of existing components and new configurations of existing systems

The House of Representatives: “Grand Depository of the Democratic Principle”?

The solar simulator is the key facility for indoor research of solar PV cells, solar heat collectors, space craft and CSP systems. This paper classifies the four types of solar simulators based on their characteristics and their design objects: space solar simulator, standard PV cell testing solar simulator, collector testing solar simulator and high-flux solar simulator. The review of solar simulator developments is mainly based on the developments of light sources and optical concentrators. The light source is the most important component for a solar simulator design; carbon arc lamp, metal halide arc lamp, quartz tungsten halogen lamp, xenon arc lamp, mercury xenon lamp, argon arc lamp and light-emitting diode lamp (LED) are used to be chosen as the light sources to meet the various requirements for the design objects. The optical concentrator is another key component; ellipsoidal reflector, compound parabolic concentrator (CPC), light cone, hyperboloid concentrator, parabolic dish concentrator and Fresnel lens are also reviewed in this paper. Finally, the near future developments of these four type solar simulators are discussed based on the requirements of research and the available technology of light sources and optical concentrators.QC 20141017</p

A Fault Taxonomy for Component-Based Software

AbstractComponent technology is increasingly used to develop modular, configurable, and reusable systems. The problem of design and implement component-based systems is addressed by many models, methodologies, tools, and frameworks. On the contrary, analysis and test are not adequately supported yet. In general, a coherent fault taxonomy is a key starting point for providing techniques and methods for assessing the quality of software and in particular of component-based systems. This paper proposes a fault taxonomy to be used to develop and evaluate testing and analysis techniques for component-based software

Testing systems of identical components

We consider the problem of testing sequentially the components of a multi-component reliability system in order to figure out the state of the system via costly tests. In particular, systems with identical components are considered. The notion of lexicographically large binary decision trees is introduced and a heuristic algorithm based on that notion is proposed. The performance of the heuristic algorithm is demonstrated by computational results, for various classes of functions. In particular, in all 200 random cases where the underlying function is a threshold function, the proposed heuristic produces optimal solutions