160 research outputs found
06172 Abstracts Collection -- Directed Model Checking
From 26.04.06 to 29.04.06, the Dagstuhl Seminar 06172 ``Directed Model Checking\u27\u27
was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Protocol verification with heuristic search
We present an approach to reconcile explicit state model checking and heuristic directed search and provide experimental evidence that the model checking problem for concurrent systems, such as communications protocols, can be solved more efficiently, since finding a state violating a property can be understood as a directed search problem. In our work we combine the expressive power and implementation efficiency of the SPIN model checker with the HSF heuristic search workbench, yielding the HSF-SPIN tool that we have implemented. We start off from the A* algorithm and some of its derivatives and define heuristics for various system properties that guide the search so that it finds error states faster. In this paper we focus on safety properties and provide heuristics for invariant and assertion violation and deadlock detection. We provide experimental results for applying HSF-SPIN to two toy protocols and one real world protocol, the CORBA GIOP protocol
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
Counterexample-guided repair aims at creating neural networks with
mathematical safety guarantees, facilitating the application of neural networks
in safety-critical domains. However, whether counterexample-guided repair is
guaranteed to terminate remains an open question. We approach this question by
showing that counterexample-guided repair can be viewed as a robust
optimisation algorithm. While termination guarantees for neural network repair
itself remain beyond our reach, we prove termination for more restrained
machine learning models and disprove termination in a general setting. We
empirically study the practical implications of our theoretical results,
demonstrating the suitability of common verifiers and falsifiers for repair
despite a disadvantageous theoretical result. Additionally, we use our
theoretical insights to devise a novel algorithm for repairing linear
regression models based on quadratic programming, surpassing existing
approaches.Comment: Accepted at ICML 2023. 9 pages + 13 pages appendix, 8 figure
symQV: Automated Symbolic Verification of Quantum Programs
We present symQV, a symbolic execution framework for writing and verifying
quantum computations in the quantum circuit model. symQV can automatically
verify that a quantum program complies with a first-order specification. We
formally introduce a symbolic quantum program model. This allows to encode the
verification problem in an SMT formula, which can then be checked with a
delta-complete decision procedure. We also propose an abstraction technique to
speed up the verification process. Experimental results show that the
abstraction improves symQV's scalability by an order of magnitude to quantum
programs with 24 qubits (a 2^24-dimensional state space).Comment: This is the extended version of a paper with the same title that
appeared at FM 2023. Tool available at doi.org/10.5281/zenodo.740032
SpecAttack: Specification-Based Adversarial Training for Deep Neural Networks
Safety specification-based adversarial training aims to generate examples
violating a formal safety specification and therefore provides approaches for
repair. The need for maintaining high prediction accuracy while ensuring the
save behavior remains challenging. Thus we present SpecAttack, a
query-efficient counter-example generation and repair method for deep neural
networks. Using SpecAttack allows specifying safety constraints on the model to
find inputs that violate these constraints. These violations are then used to
repair the neural network via re-training such that it becomes provably safe.
We evaluate SpecAttack's performance on the task of counter-example generation
and repair. Our experimental evaluation demonstrates that SpecAttack is in most
cases more query-efficient than comparable attacks, yields counter-examples of
higher quality, with its repair technique being more efficient, maintaining
higher functional correctness, and provably guaranteeing safety specification
compliance
Verl\"assliche Software im 21. Jahrhundert
Software is the main innovation driver in many different areas, like cloud
services, autonomous driving, connected medical devices, and high-frequency
trading. All these areas have in common that they require high dependability.
In this paper, we discuss challenges and research directions imposed by these
new areas on guaranteeing the dependability. On the one hand challenges include
characteristics of the systems themselves, e. g., open systems and ad-hoc
structures. On the other hand, we see new aspects of dependability like
behavioral traceability.Comment: 6 pages, in German, 1 figur
Trail-directed model checking
HSF-SPIN is a Promela model checker based on heuristic search strategies. It utilizes heuristic estimates in order to direct the search for finding software bugs in concurrent systems. As a consequence, HSF-SPIN is able to find shorter trails than blind depth-first search.
This paper contributes an extension to the paradigm of directed model checking to shorten already established unacceptable long error trails. This approach has been implemented in HSF-SPIN. For selected benchmark and industrial communication protocols experimental evidence is given that trail-directed model-checking effectively shortcuts existing witness paths
QuantUM: Quantitative Safety Analysis of UML Models
When developing a safety-critical system it is essential to obtain an
assessment of different design alternatives. In particular, an early safety
assessment of the architectural design of a system is desirable. In spite of
the plethora of available formal quantitative analysis methods it is still
difficult for software and system architects to integrate these techniques into
their every day work. This is mainly due to the lack of methods that can be
directly applied to architecture level models, for instance given as UML
diagrams. Also, it is necessary that the description methods used do not
require a profound knowledge of formal methods. Our approach bridges this gap
and improves the integration of quantitative safety analysis methods into the
development process. All inputs of the analysis are specified at the level of a
UML model. This model is then automatically translated into the analysis model,
and the results of the analysis are consequently represented on the level of
the UML model. Thus the analysis model and the formal methods used during the
analysis are hidden from the user. We illustrate the usefulness of our approach
using an industrial strength case study.Comment: In Proceedings QAPL 2011, arXiv:1107.074
Dagstuhl-Manifest zur Strategischen Bedeutung des Software Engineering in Deutschland
Im Rahmen des Dagstuhl Perspektiven Workshop 05402 "Challenges for Software Engineering Research" haben fĂÂŒhrende Software Engineering Professoren den derzeitigen Stand der Softwaretechnik in Deutschland charakterisiert und Handlungsempfehlungen fĂÂŒr Wirtschaft, Forschung und Politik abgeleitet. Das Manifest fasst die diese Empfehlungen und die Bedeutung und Entwicklung des Fachgebiets prĂ€gnant zusammen
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