Compositional synthesis of temporal fault trees from state machines

Dependability analysis of a dynamic system which is embedded with several complex interrelated components raises two main problems. First, it is difficult to represent in a single coherent and complete picture how the system and its constituent parts behave in conditions of failure. Second, the analysis can be unmanageable due to a considerable number of failure events, which increases with the number of components involved. To remedy this problem, in this paper we outline an analysis approach that converts failure behavioural models (state machines) to temporal fault trees (TFTs), which can then be analysed using Pandora -- a recent technique for introducing temporal logic to fault trees. The approach is compositional and potentially more scalable, as it relies on the synthesis of large system TFTs from smaller component TFTs. We show, by using a Generic Triple Redundant (GTR) system, how the approach enables a more accurate and full analysis of an increasingly complex system

Quantitative multi-objective verification for probabilistic systems

We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies

A Component-oriented Framework for Autonomous Agents

The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components

Models for CSP with availability information

We consider models of CSP based on recording what events are available as possible alternatives to the events that are actually performed. We present many different varieties of such models. For each, we give a compositional semantics, congruent to the operational semantics, and prove full abstraction and no-junk results. We compare the expressiveness of the different models.Comment: In Proceedings EXPRESS'10, arXiv:1011.601

Homemade yeast by using tropical fruits through fermentation process

Yeast is one type of bacteria that is used in baking industry and as the crucial parameters to determine the softness of the bread. Many tropical fruits can be used as a medium to produce yeast. The main goal of this study is to produce homemade yeast by different types of fruits. The methods used to produce yeast are fermentation and separation process, where tropical fruits such as the banana, pineapple and raisin were used. The fruits are fermented for seven days, and they are supplied daily with calculated amount of sugar and flour. The yeast produced are then used in making apam, where three parameters are measured which are the volume of apam, the flavour and the aroma of apam. The best tropical fruits to produce yeast, arranged in order are raisin, banana and pineapple. We are unable to study further on the starfruit because it is seasonally unavailable during the research period. The result of the present study would provide knowledge and information about tropical fruits as homemade yeast. Additionally, this study will produce significant and relevant information for future studies regarding to homemade yeast

Model checking probabilistic and stochastic extensions of the pi-calculus

We present an implementation of model checking for probabilistic and stochastic extensions of the pi-calculus, a process algebra which supports modelling of concurrency and mobility. Formal verification techniques for such extensions have clear applications in several domains, including mobile ad-hoc network protocols, probabilistic security protocols and biological pathways. Despite this, no implementation of automated verification exists. Building upon the pi-calculus model checker MMC, we first show an automated procedure for constructing the underlying semantic model of a probabilistic or stochastic pi-calculus process. This can then be verified using existing probabilistic model checkers such as PRISM. Secondly, we demonstrate how for processes of a specific structure a more efficient, compositional approach is applicable, which uses our extension of MMC on each parallel component of the system and then translates the results into a high-level modular description for the PRISM tool. The feasibility of our techniques is demonstrated through a number of case studies from the pi-calculus literature