Coverage-guided test generation for continuous and hybrid systems

Dang, Thao; Nahhal, Tarik

doi:10.1007/s10703-009-0066-0

Cited by 75 publications

(50 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach of [38], instead, use statistical methods borrowed from machine learning to perform inference of the reachable set with statistical error guarantees. Other methods work for more general imprecise limit models; for example, the procedure of [39]- [41] constructs an under-approximation of the reachable set using a Monte-Carlo sampling method.…”

Section: A Related Work On Numerical Methodsmentioning

confidence: 99%

Mean Field Approximation of Uncertain Stochastic Models

Bortolussi

Gast

2016

2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

View full text Add to dashboard Cite

Abstract-We consider stochastic models in presence of uncertainty, originating from lack of knowledge of parameters or by unpredictable effects of the environment. We focus on population processes, encompassing a large class of systems, from queueing networks to epidemic spreading. We set up a formal framework for imprecise stochastic processes, where some parameters are allowed to vary in time within a given domain, but with no further constraint. We then consider the limit behaviour of these systems as the population size goes to infinity. We prove that this limit is given by a differential inclusion that can be constructed from the (imprecise) drift. We provide results both for the transient and the steady state behaviour. Finally, we discuss different approaches to compute bounds of the soobtained differential inclusions, proposing an effective controltheoretic method based on Pontryagin principle for transient bounds. This provides an efficient approach for the analysis and design of large-scale uncertain and imprecise stochastic models. The theoretical results are accompanied by an in-depth analysis of an epidemic model and a queueing network. These examples demonstrate the applicability of the numerical methods and the tightness of the approximation.

show abstract

Section: A Related Work On Numerical Methodsmentioning

confidence: 99%

Mean Field Approximation of Uncertain Stochastic Models

Bortolussi

Gast

2016

2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

View full text Add to dashboard Cite

show abstract

“…In addition, we can prove that by appropriately sampling the input set, the completeness property of our algorithm is preserved [13]). It is important to emphasize that the function ContinuousSucc must assure that the trajectory segment from x near remains in the staying set of the current location.…”

Section: Computing Continuous and Discrete Successorsmentioning

confidence: 95%

How to Design Extended Finite State Machine Test Models in Java

Utting¹

2017

Model-Based Testing for Embedded Systems

View full text Add to dashboard Cite

“…Techniques such as [15], [18], [28] use simulation based approaches to address the problem of uniformly exploring a continuous state space. These techniques typically 1 explore the trajectories in one direction: forward from the initial set, or backwards from the error set.…”

Section: ) Direct Multiple Shootingmentioning

confidence: 99%

A trajectory splicing approach to concretizing counterexamples for hybrid systems

Zutshi

Sankaranarayanan

Deshmukh³

et al. 2013

52nd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Abstract-This paper examines techniques for finding falsifying trajectories of hybrid systems using an approach that we call trajectory splicing. Many formal verification techniques for hybrid systems, including flowpipe construction, can identify plausible abstract counterexamples for property violations. However, there is often a gap between the reported abstract counterexamples and the concrete system trajectories. Our approach starts with a candidate sequence of disconnected trajectory segments, each segment lying inside a discrete mode. However, such disconnected segments do not form concrete violations due to the gaps that exist between the ending state of one segment and the starting state of the subsequent segment. Therefore, trajectory splicing uses local optimization to minimize the gap between these segments, effectively splicing them together to form a concrete trajectory.We demonstrate the use of our approach for falsifying safety properties of hybrid systems using standard optimization techniques. As such, our approach is not restricted to linear systems. We compare our approach with other falsification approaches including uniform random sampling and a robustness guided falsification approach used in the tool S-Taliro. Our preliminary evaluation clearly shows the potential of our approach to search for candidate trajectory segments and use them to find concrete property violations.

show abstract

Coverage-guided test generation for continuous and hybrid systems

Cited by 75 publications

References 24 publications

Mean Field Approximation of Uncertain Stochastic Models

Mean Field Approximation of Uncertain Stochastic Models

How to Design Extended Finite State Machine Test Models in Java

A trajectory splicing approach to concretizing counterexamples for hybrid systems

Contact Info

Product

Resources

About