2,749 research outputs found
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Dynamic reconfiguration of GCM components
We detail in this report past research and current/future developments in formal specification of Grid component systems by temporal logic and consequent resolution technique, for an automated dynamic reconfiguration of components. It is analysed the specification procedure of GCM (Grid Component Model) components and infrastructure in respect to their state behaviour, and the verification process in a dynamic and reconfigurable distributed system. Furthermore it is demonstrated how an automata based method is used to achieve the specification, as well as how the enrichment of the temporal specification language of Computation Tree Logic CTL with the ability to capture norms, allows to formally define the concept of reconfiguration
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
Verifying Temporal Properties of Reactive Systems by Transformation
We show how program transformation techniques can be used for the
verification of both safety and liveness properties of reactive systems. In
particular, we show how the program transformation technique distillation can
be used to transform reactive systems specified in a functional language into a
simplified form that can subsequently be analysed to verify temporal properties
of the systems. Example systems which are intended to model mutual exclusion
are analysed using these techniques with respect to both safety (mutual
exclusion) and liveness (non-starvation), with the errors they contain being
correctly identified.Comment: In Proceedings VPT 2015, arXiv:1512.02215. This work was supported,
in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero - the Irish
Software Engineering Research Centre (www.lero.ie), and by the School of
Computing, Dublin City Universit
Generalization Strategies for the Verification of Infinite State Systems
We present a method for the automated verification of temporal properties of
infinite state systems. Our verification method is based on the specialization
of constraint logic programs (CLP) and works in two phases: (1) in the first
phase, a CLP specification of an infinite state system is specialized with
respect to the initial state of the system and the temporal property to be
verified, and (2) in the second phase, the specialized program is evaluated by
using a bottom-up strategy. The effectiveness of the method strongly depends on
the generalization strategy which is applied during the program specialization
phase. We consider several generalization strategies obtained by combining
techniques already known in the field of program analysis and program
transformation, and we also introduce some new strategies. Then, through many
verification experiments, we evaluate the effectiveness of the generalization
strategies we have considered. Finally, we compare the implementation of our
specialization-based verification method to other constraint-based model
checking tools. The experimental results show that our method is competitive
with the methods used by those other tools. To appear in Theory and Practice of
Logic Programming (TPLP).Comment: 24 pages, 2 figures, 5 table
Techniques for automated parameter estimation in computational models of probabilistic systems
The main contribution of this dissertation is the design of two new algorithms for automatically synthesizing values of numerical parameters of computational models of complex stochastic systems such that the resultant model meets user-specified behavioral specifications. These algorithms are designed to operate on probabilistic systems – systems that, in general, behave differently under identical conditions. The algorithms work using an approach that combines formal verification and mathematical optimization to explore a model\u27s parameter space. The problem of determining whether a model instantiated with a given set of parameter values satisfies the desired specification is first defined using formal verification terminology, and then reformulated in terms of statistical hypothesis testing. Parameter space exploration involves determining the outcome of the hypothesis testing query for each parameter point and is guided using simulated annealing. The first algorithm uses the sequential probability ratio test (SPRT) to solve the hypothesis testing problems, whereas the second algorithm uses an approach based on Bayesian statistical model checking (BSMC). The SPRT-based parameter synthesis algorithm was used to validate that a given model of glucose-insulin metabolism has the capability of representing diabetic behavior by synthesizing values of three parameters that ensure that the glucose-insulin subsystem spends at least 20 minutes in a diabetic scenario. The BSMC-based algorithm was used to discover the values of parameters in a physiological model of the acute inflammatory response that guarantee a set of desired clinical outcomes. These two applications demonstrate how our algorithms use formal verification, statistical hypothesis testing and mathematical optimization to automatically synthesize parameters of complex probabilistic models in order to meet user-specified behavioral propertie
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