331 research outputs found
Extensions of statecharts : with probability, time, and stochastic timing
Statecharts are a graphical language to describe the behaviour of a system. For example, in the UML, a statechart can be used to describe the behaviour of an object. Model checking is a method to verify automatically whether a system satisfies some desired property.\ud
The goal of this thesis is: To use statecharts to render model checking more widely usable.\ud
We show this in two respects: For real-time statecharts, we provide a property language that fits nicely with the features of statecharts. For probabilistic model checking, we provide an extension of statecharts as input language
Dependability checking with StoCharts: Is train radio reliable enough for trains?
Performance, dependability and quality of service (QoS) are prime aspects of the UML modelling domain. To capture these aspects effectively in the design phase, we have recently proposed STOCHARTS, a conservative extension of UML statechart diagrams. In this paper, we apply the STOCHART formalism to a safety critical design problem. We model a part of the European Train Control System specification, focusing on the risks of wireless communication failures in future high-speed cross-European trains. Stochastic model checking with the model checker PROVER enables us to derive constraints under which the central quality requirements are satisfied by the STOCHART model. The paper illustrates the flexibility and maturity of STOCHARTS to model real problems in safety critical system design
A Holistic Approach in Embedded System Development
We present pState, a tool for developing "complex" embedded systems by
integrating validation into the design process. The goal is to reduce
validation time. To this end, qualitative and quantitative properties are
specified in system models expressed as pCharts, an extended version of
hierarchical state machines. These properties are specified in an intuitive way
such that they can be written by engineers who are domain experts, without
needing to be familiar with temporal logic. From the system model, executable
code that preserves the verified properties is generated. The design is
documented on the model and the documentation is passed as comments into the
generated code. On the series of examples we illustrate how models and
properties are specified using pState.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338
A comparative reliability analysis of ETCS train radio communications
StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and were applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Möbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study
From StoCharts to MoDeST: a comparative reliability analysis of train radio communications
StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and have been applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Möbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study
A model checker for performance and dependability properties
Markov chains are widely used in the context of
performance and reliability evaluation of systems of various
nature. Model checking of such chains with respect to
a given (branching) temporal logic formula has been proposed
for both the discrete [8] and the continuous time setting
[1], [3]. In this short paper, we describe the prototype
model checker for discrete and continuous-time
Markov chains, where properties are expressed in appropriate
extensions of CTL.We illustrate the general benefits
of this approach and discuss the structure of the tool
A Probabilistic Extension of UML-B
This paper extends the graphical and formal language of UML-B to provide the ability to model probabilities. Discrete probabilities, interval probabilities, and stochastic delays are added to the UML-B's state-machine syntax, and their corresponding semantics are defined in Event-B. In addition, as a secondary contribution, UML-B (probabilistic) state-machine models are defined as MDP (Markov Decision Process) models in order to provide a means of quantitative verification in PRISM (Probabilistic Symbolic Model Checker). As an important feature of the proposed method, it does not change the Event-B syntax or semantics. To evaluate this work, as a case study, the Zeroconf protocol will be modeled in the extended UML-B using the Rodin tool, and its Event-B counterpart is converted to a PRISM model. The results of evaluations indicate that this study's additions provide the capability of modeling and verification of probabilistic and stochastic systems
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
Computational Modeling, Formal Analysis, and Tools for Systems Biology.
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science
Modeling and formal verification of probabilistic reconfigurable systems
In this thesis, we propose a new approach for formal modeling and verification of adaptive probabilistic systems. Dynamic reconfigurable systems are the trend of all future technological systems, such as flight control systems, vehicle electronic systems, and manufacturing systems. In order to meet user and environmental requirements, such a dynamic reconfigurable system has to actively adjust its configuration at run-time by modifying its components and connections, while changes are detected in the internal/external execution environment. On the other hand, these changes may violate the memory usage, the required energy and the concerned real-time constraints since the behavior of the system is unpredictable. It might also make the system's functions unavailable for some time and make potential harm to human life or large financial investments. Thus, updating a system with any new configuration requires that the post reconfigurable system fully satisfies the related constraints. We introduce GR-TNCES formalism for the optimal functional and temporal specification of probabilistic reconfigurable systems under resource constraints. It enables the optimal specification of a probabilistic, energetic and memory constraints of such a system. To formally verify the correctness and the safety of such a probabilistic system specification, and the non-violation of its properties, an automatic transformation from GR-TNCES models into PRISM models is introduced. Moreover, a new approach XCTL is also proposed to formally verify reconfigurable systems. It enables the formal certification of uncompleted and reconfigurable systems. A new version of the software ZIZO is also proposed to model, simulate and verify such GR-TNCES model. To prove its relevance, the latter was applied to case studies; it was used to model and simulate the behavior of an IPV4 protocol to prevent the energy and memory resources violation. It was also used to optimize energy consumption of an automotive skid conveyor.In dieser Arbeit wird ein neuer Ansatz zur formalen Modellierung und Verifikation dynamisch rekonfigurierbarer Systeme vorgestellt. Dynamische rekonfigurierbare Systeme sind in vielen aktuellen und zukünftigen Anwendungen, wie beispielsweise Flugsteuerungssystemen, Fahrzeugelektronik und Fertigungssysteme zu finden. Diese Systeme weisen ein probabilistisches, adaptives Verhalten auf. Um die Benutzer- und Umgebungsbedingungen kontinuierlich zu erfüllen, muss ein solches System seine Konfiguration zur Laufzeit aktiv anpassen, indem es seine Komponenten, Verbindungen zwischen Komponenten und seine Daten modifiziert (adaptiv), sobald Änderungen in der internen oder externen Ausführungsumgebung erkannt werden (probabilistisch). Diese Anpassungen dürfen Beschränkungen bei der Speichernutzung, der erforderlichen Energie und bestehende Echtzeitbedingungen nicht verletzen. Eine nicht geprüfte Rekonfiguration könnte dazu führen, dass die Funktionen des Systems für einige Zeit nicht verfügbar wären und potenziell menschliches Leben gefährdet würde oder großer finanzieller Schaden entstünde. Somit erfordert das Aktualisieren eines Systems mit einer neuen Konfiguration, dass das rekonfigurierte System die zugehörigen Beschränkungen vollständig einhält. Um dies zu überprüfen, wird in dieser Arbeit der GR-TNCES-Formalismus, eine Erweiterung von Petrinetzen, für die optimale funktionale und zeitliche Spezifikation probabilistischer rekonfigurierbarer Systeme unter Ressourcenbeschränkungen vorgeschlagen. Die entstehenden Modelle sollen über probabilistische model checking verifiziert werden. Dazu eignet sich die etablierte Software PRISM. Um die Verifikation zu ermöglichen wird in dieser Arbeit ein Verfahren zur Transformation von GR-TNCES-Modellen in PRISM-Modelle beschrieben. Eine neu eingeführte Logik (XCTL) erlaubt zudem die einfache Beschreibung der zu prüfenden Eigenschaften. Die genannten Schritte wurden in einer Softwareumgebung für den automatisierten Entwurf, die Simulation und die formale Verifikation (durch eine automatische Transformation nach PRISM) umgesetzt. Eine Fallstudie zeigt die Anwendung des Verfahren
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