@inproceedings{631,
abstract = {Template polyhedra generalize intervals and octagons to polyhedra whose facets are orthogonal to a given set of arbitrary directions. They have been employed in the abstract interpretation of programs and, with particular success, in the reachability analysis of hybrid automata. While previously, the choice of directions has been left to the user or a heuristic, we present a method for the automatic discovery of directions that generalize and eliminate spurious counterexamples. We show that for the class of convex hybrid automata, i.e., hybrid automata with (possibly nonlinear) convex constraints on derivatives, such directions always exist and can be found using convex optimization. We embed our method inside a CEGAR loop, thus enabling the time-unbounded reachability analysis of an important and richer class of hybrid automata than was previously possible. We evaluate our method on several benchmarks, demonstrating also its superior efficiency for the special case of linear hybrid automata.},
author = {Bogomolov, Sergiy and Frehse, Goran and Giacobbe, Mirco and Henzinger, Thomas A},
isbn = {978-366254576-8},
location = {Uppsala, Sweden},
pages = {589 -- 606},
publisher = {Springer},
title = {{Counterexample guided refinement of template polyhedra}},
doi = {10.1007/978-3-662-54577-5_34},
volume = {10205},
year = {2017},
}
@inproceedings{636,
abstract = {Signal regular expressions can specify sequential properties of real-valued signals based on threshold conditions, regular operations, and duration constraints. In this paper we endow them with a quantitative semantics which indicates how robustly a signal matches or does not match a given expression. First, we show that this semantics is a safe approximation of a distance between the signal and the language defined by the expression. Then, we consider the robust matching problem, that is, computing the quantitative semantics of every segment of a given signal relative to an expression. We present an algorithm that solves this problem for piecewise-constant and piecewise-linear signals and show that for such signals the robustness map is a piecewise-linear function. The availability of an indicator describing how robustly a signal segment matches some regular pattern provides a general framework for quantitative monitoring of cyber-physical systems.},
author = {Bakhirkin, Alexey and Ferrere, Thomas and Maler, Oded and Ulus, Dogan},
editor = {Abate, Alessandro and Geeraerts, Gilles},
isbn = {978-331965764-6},
location = {Berlin, Germany},
pages = {189 -- 206},
publisher = {Springer},
title = {{On the quantitative semantics of regular expressions over real-valued signals}},
doi = {10.1007/978-3-319-65765-3_11},
volume = {10419},
year = {2017},
}
@inproceedings{941,
abstract = {Recently there has been a proliferation of automated program repair (APR) techniques, targeting various programming languages. Such techniques can be generally classified into two families: syntactic- and semantics-based. Semantics-based APR, on which we focus, typically uses symbolic execution to infer semantic constraints and then program synthesis to construct repairs conforming to them. While syntactic-based APR techniques have been shown successful on bugs in real-world programs written in both C and Java, semantics-based APR techniques mostly target C programs. This leaves empirical comparisons of the APR families not fully explored, and developers without a Java-based semantics APR technique. We present JFix, a semantics-based APR framework that targets Java, and an associated Eclipse plugin. JFix is implemented atop Symbolic PathFinder, a well-known symbolic execution engine for Java programs. It extends one particular APR technique (Angelix), and is designed to be sufficiently generic to support a variety of such techniques. We demonstrate that semantics-based APR can indeed efficiently and effectively repair a variety of classes of bugs in large real-world Java programs. This supports our claim that the framework can both support developers seeking semantics-based repair of bugs in Java programs, as well as enable larger scale empirical studies comparing syntactic- and semantics-based APR targeting Java. The demonstration of our tool is available via the project website at: https://xuanbachle.github.io/semanticsrepair/ },
author = {Le, Xuan and Chu, Duc Hiep and Lo, David and Le Goues, Claire and Visser, Willem},
booktitle = {Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis},
location = {Santa Barbara, CA, United States},
pages = {376 -- 379 },
publisher = {ACM},
title = {{JFIX: Semantics-based repair of Java programs via symbolic PathFinder}},
doi = {10.1145/3092703.3098225},
year = {2017},
}
@inproceedings{663,
abstract = {In this paper, we propose an approach to automatically compute invariant clusters for nonlinear semialgebraic hybrid systems. An invariant cluster for an ordinary differential equation (ODE) is a multivariate polynomial invariant g(u→, x→) = 0, parametric in u→, which can yield an infinite number of concrete invariants by assigning different values to u→ so that every trajectory of the system can be overapproximated precisely by the intersection of a group of concrete invariants. For semialgebraic systems, which involve ODEs with multivariate polynomial right-hand sides, given a template multivariate polynomial g(u→, x→), an invariant cluster can be obtained by first computing the remainder of the Lie derivative of g(u→, x→) divided by g(u→, x→) and then solving the system of polynomial equations obtained from the coefficients of the remainder. Based on invariant clusters and sum-of-squares (SOS) programming, we present a new method for the safety verification of hybrid systems. Experiments on nonlinear benchmark systems from biology and control theory show that our approach is efficient. },
author = {Kong, Hui and Bogomolov, Sergiy and Schilling, Christian and Jiang, Yu and Henzinger, Thomas A},
booktitle = {Proceedings of the 20th International Conference on Hybrid Systems},
isbn = {978-145034590-3},
location = {Pittsburgh, PA, United States},
pages = {163 -- 172},
publisher = {ACM},
title = {{Safety verification of nonlinear hybrid systems based on invariant clusters}},
doi = {10.1145/3049797.3049814},
year = {2017},
}
@article{1066,
abstract = {Simulation is an attractive alternative to language inclusion for automata as it is an under-approximation of language inclusion, but usually has much lower complexity. Simulation has also been extended in two orthogonal directions, namely, (1) fair simulation, for simulation over specified set of infinite runs; and (2) quantitative simulation, for simulation between weighted automata. While fair trace inclusion is PSPACE-complete, fair simulation can be computed in polynomial time. For weighted automata, the (quantitative) language inclusion problem is undecidable in general, whereas the (quantitative) simulation reduces to quantitative games, which admit pseudo-polynomial time algorithms.
In this work, we study (quantitative) simulation for weighted automata with Büchi acceptance conditions, i.e., we generalize fair simulation from non-weighted automata to weighted automata. We show that imposing Büchi acceptance conditions on weighted automata changes many fundamental properties of the simulation games, yet they still admit pseudo-polynomial time algorithms.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan and Velner, Yaron},
journal = {Information and Computation},
number = {2},
pages = {143 -- 166},
publisher = {Elsevier},
title = {{Quantitative fair simulation games}},
doi = {10.1016/j.ic.2016.10.006},
volume = {254},
year = {2017},
}