Process Algebras

Process Algebras are mathematically rigorous languages with well defined semantics that permit describing and verifying properties of concurrent communicating systems. They can be seen as models of processes, regarded as agents that act and interact continuously with other similar agents and with their common environment. The agents may be real-world objects (even people), or they may be artifacts, embodied perhaps in computer hardware or software systems. Many different approaches (operational, denotational, algebraic) are taken for describing the meaning of processes. However, the operational approach is the reference one. By relying on the so called Structural Operational Semantics (SOS), labelled transition systems are built and composed by using the different operators of the many different process algebras. Behavioral equivalences are used to abstract from unwanted details and identify those systems that react similarly to external experiments

Metric Semantics and Full Abstractness for Action Refinement and Probabilistic Choice

This paper provides a case-study in the field of metric semantics for probabilistic programming. Both an operational and a denotational semantics are presented for an abstract process language L_pr, which features action refinement and probabilistic choice. The two models are constructed in the setting of complete ultrametric spaces, here based on probability measures of compact support over sequences of actions. It is shown that the standard toolkit for metric semantics works well in the probabilistic context of L_pr, e.g. in establishing the correctness of the denotational semantics with respect to the operational one. In addition, it is shown how the method of proving full abstraction --as proposed recently by the authors for a nondeterministic language with action refinement-- can be adapted to deal with the probabilistic language L_pr as well

STAIRS - Understanding and Developing Specifications Expressed as UML Interaction Diagrams

STAIRS is a method for the step-wise, compositional development of interactions in the setting of UML 2.x. UML 2.x interactions, such as sequence diagrams and interaction overview diagrams, are seen as intuitive ways of describing communication between different parts of a system, and between a system and its users. STAIRS addresses the challenges of harmonizing intuition and formal reasoning by providing a precise understanding of the partial nature of interactions, and of how this kind of incomplete specifications may be consistently refined into more complete specifications. For understanding individual interaction diagrams, STAIRS defines a denotational trace semantics for the main constructs of UML 2.x interactions. The semantic model takes into account the partiality of interactions, and the formal semantics of STAIRS is faithful to the informal semantics given in the UML 2.x standard. For developing UML 2.x interactions, STAIRS defines a number of refinement relations corresponding to basic system development steps. STAIRS also defines matching compliance relations, for relating interactions to real computer systems. An important feature of STAIRS is the distinction between underspecification and inherent nondeterminism. Underspecification means that there are several possible behaviours serving the same overall purpose, and that it is sufficient for a computer system to perform only one of these. On the other hand, inherent nondeterminism is used to capture alternative behaviours that must all be possible for an implementation. A typical example is the tossing of a coin, where both heads and tails should be possible outcomes. In some cases, using inherent nondeterminism may also be essential for ensuring the necessary security properties of a system

Towards a proof of the Kahn principle for linear dynamic networks

We consider dynamic Kahn-like data flow networks, i.e. networks consisting of deterministic processes each of which is able to expand into a subnetwork. The Kahn principle states that such networks are deterministic, i.e. that for each network we have that each execution provided with the same input delivers the same output. Moreover, the principle states that the output streams of such networks can be obtained as the smallest fixed point of a suitable operator derived from the network specification. This paper is meant as a first step towards a proof of this principle. For a specific subclass of dynamic networks, linear arrays of processes, we define a transition system yielding an operational semantics which defines the meaning of a net as the set of all possible interleaved executions. We then prove that, although on the execution level there is much nondeterminism, this nondeterminism disappears when viewing the system as a transformation from an input stream to an output stream. This result is obtained from the graph of all computations. For any configuration such a graph can be constructed. All computation sequences that start from this configuration and that are generated by the operational semantics are embedded in it

The consistency of a noninterleaving and an interleaving model for full TCSP

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