TIPPtool: Compositional Specification and Analysis of Markovian Performance Models

In this short paper we briefly describe a tool which is based on a Markovian stochastic process algebra. The tool offers both model specification and quantitative model analysis in a compositional fashion, wrapped in a userfriendly graphical front-end

Compositional Performance Modelling with the TIPPtool

Stochastic process algebras have been proposed as compositional specification formalisms for performance models. In this paper, we describe a tool which aims at realising all beneficial aspects of compositional performance modelling, the TIPPtool. It incorporates methods for compositional specification as well as solution, based on state-of-the-art techniques, and wrapped in a user-friendly graphical front end. Apart from highlighting the general benefits of the tool, we also discuss some lessons learned during development and application of the TIPPtool. A non-trivial model of a real life communication system serves as a case study to illustrate benefits and limitations

On the use of observation equivalence in synthesis abstraction

In a previous paper we introduced the notion of synthesis abstraction, which allows efficient compositional synthesis of maximally permissive supervisors for large-scale systems of composed finite-state automata. In the current paper, observation equivalence is studied in relation to synthesis abstraction. It is shown that general observation equivalence is not useful for synthesis abstraction. Instead, we introduce additional conditions strengthening observation equivalence, so that it can be used with the compositional synthesis method. The paper concludes with an example showing the suitability of these relations to achieve substantial state reduction while computing a modular supervisor

Compositional reliability analysis using probabilistic component automata

Compositionality is a key property in the development and analy- sis of component-based systems. In non-probabilistic formalisms such as Labelled Transition Systems (LTS) the functional behaviour of a system can be readily constructed from the behaviours of its parts. However, this is not true for probabilistic extensions of LTS, which are necessary to analyse non-functional properties such as reliability. We propose Probabilistic Component Automata (PCA) as a proba- bilistic extension to Interface Automata to automatically construct a system model by composing models of its sub-components. In par- ticular, we focus on modelling failure scenarios, failure handling and failure propagation. Additionally, we propose a novel algorithm based on Compositional Reachability Analysis to mitigate the well-known state-explosion problem associated with composable models. Both Probabilistic Component Automata and the reduction algorithm have been implemented in the LTSA tool

Compositional synthesis of discrete event systems via synthesis equivalence

A two-pass algorithm for compositional synthesis of modular supervisors for largescale systems of composed finite-state automata is proposed. The first pass provides an efficient method to determine whether a supervisory control problem has a solution, without explicitly constructing the synchronous composition of all components. If a solution exists, the second pass yields an over-approximation of the least restrictive solution which, if nonblocking, is a modular representation of the least restrictive supervisor. Using a new type of equivalence of nondeterministic processes, called synthesis equivalence, a wide range of abstractions can be employed to mitigate state-space explosion throughout the algorithm