Verification for Timed Automata extended with Unbounded Discrete Data Structures

We study decidability of verification problems for timed automata extended with unbounded discrete data structures. More detailed, we extend timed automata with a pushdown stack. In this way, we obtain a strong model that may for instance be used to model real-time programs with procedure calls. It is long known that the reachability problem for this model is decidable. The goal of this paper is to identify subclasses of timed pushdown automata for which the language inclusion problem and related problems are decidable

Minimum-Cost Reachability for Priced Timed Automata

This paper introduces the model of linearly priced timed automata as an extension of timed automata, with prices on both transitions and locations. For this model we consider the minimum-cost reachability problem: i.e. given a linearly priced timed automaton and a targetstate, determine the minimum cost of executions from the initial state to the target state. This problem generalizes the minimum-time reachability problem for ordinary timed automata. We prove decidability of this problem by offering an algorithmic solution, which is based on a combination of branch-and-bound techniques and a new notion of priced regions. The latter allows symbolic representation and manipulation of reachable states together with the cost of reaching them.Keywords: Timed Automata, Verification, Data Structures, Algorithms,Optimization

Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Conformance Verification of Normative Specifications using C-O Diagrams

C-O Diagrams have been introduced as a means to have a visual representation of normative texts and electronic contracts, where it is possible to represent the obligations, permissions and prohibitions of the different signatories, as well as what are the penalties in case of not fulfillment of their obligations and prohibitions. In such diagrams we are also able to represent absolute and relative timing constrains. In this paper we consider a formal semantics for C-O Diagrams based on a network of timed automata and we present several relations to check the consistency of a contract in terms of realizability, to analyze whether an implementation satisfies the requirements defined on its contract, and to compare several implementations using the executed permissions as criteria.Comment: In Proceedings FLACOS 2012, arXiv:1209.169

Formal and Informal Methods for Multi-Core Design Space Exploration

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156

A Model-Derivation Framework for Software Analysis

Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581