4,027 research outputs found
CoInDiVinE: Parallel Distributed Model Checker for Component-Based Systems
CoInDiVinE is a tool for parallel distributed model checking of interactions
among components in hierarchical component-based systems. The tool extends the
DiVinE framework with a new input language (component-interaction automata) and
a property specification logic (CI-LTL). As the language differs from the input
language of DiVinE, our tool employs a new state space generation algorithm
that also supports partial order reduction. Experiments indicate that the tool
has good scaling properties when run in parallel setting.Comment: In Proceedings PDMC 2011, arXiv:1111.006
Contexts, refinement and determinism
In this paper we have been influenced by those who take an āengineering viewā of the problem of designing systems, i.e. a view that is motivated by what someone designing a real system will be concerned with, and what questions will arise as they work on their design. Specifically, we have borrowed from the testing work of Hennessy, de Nicola and van Glabbeek, e.g. [13, 5, 21, 40, 39].
Here we concentrate on one fundamental part of the engineering view and where consideration of it leads. The aspects we are concerned with are computational entities in contexts, observed by users. This leads to formalising design steps that are often left informal, and that in turn gives insights into non-determinism and ultimately leads to being able to use refinement in situations where existing techniques fail
Learning Markov Decision Processes for Model Checking
Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345
- ā¦