796 research outputs found
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
A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System
Self-adaptation is a promising approach to manage the complexity of modern
software systems. A self-adaptive system is able to adapt autonomously to
internal dynamics and changing conditions in the environment to achieve
particular quality goals. Our particular interest is in decentralized
self-adaptive systems, in which central control of adaptation is not an option.
One important challenge in self-adaptive systems, in particular those with
decentralized control of adaptation, is to provide guarantees about the
intended runtime qualities. In this paper, we present a case study in which we
use model checking to verify behavioral properties of a decentralized
self-adaptive system. Concretely, we contribute with a formalized architecture
model of a decentralized traffic monitoring system and prove a number of
self-adaptation properties for flexibility and robustness. To model the main
processes in the system we use timed automata, and for the specification of the
required properties we use timed computation tree logic. We use the Uppaal tool
to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
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
Timed Automata Approach for Motion Planning Using Metric Interval Temporal Logic
In this paper, we consider the robot motion (or task) planning problem under
some given time bounded high level specifications. We use metric interval
temporal logic (MITL), a member of the temporal logic family, to represent the
task specification and then we provide a constructive way to generate a timed
automaton and methods to look for accepting runs on the automaton to find a
feasible motion (or path) sequence for the robot to complete the task.Comment: Full Version for ECC 201
Capturing Assumptions while Designing a Verification Model for Embedded Systems
A formal proof of a system correctness typically holds under a number of assumptions. Leaving them implicit raises the chance of using the system in a context that violates some assumptions, which in return may invalidate the correctness proof. The goal of this paper is to show how combining informal and formal techniques in the process of modelling and formal verification helps capturing these assumptions. As we focus on embedded systems, the assumptions are about the control software, the system on which the software is running and the system’s environment. We present them as a list written in natural language that supplements the formally verified embedded system model. These two together are a better argument for system correctness than each of these given separately
A Compositional Approach for Schedulability Analysis of Distributed Avionics Systems
This work presents a compositional approach for schedulability analysis of
Distributed Integrated Modular Avionics (DIMA) systems that consist of
spatially distributed ARINC-653 modules connected by a unified AFDX network. We
model a DIMA system as a set of stopwatch automata in UPPAAL to verify its
schedulability by model checking. However, direct model checking is infeasible
due to the large state space. Therefore, we introduce the compositional
analysis that checks each partition including its communication environment
individually. Based on a notion of message interfaces, a number of message
sender automata are built to model the environment for a partition. We define a
timed selection simulation relation, which supports the construction of
composite message interfaces. By using assume-guarantee reasoning, we ensure
that each task meets the deadline and that communication constraints are also
fulfilled globally. The approach is applied to the analysis of a concrete DIMA
system.Comment: In Proceedings MeTRiD 2018, arXiv:1806.09330. arXiv admin note: text
overlap with arXiv:1803.1105
A Modeling Framework for Schedulability Analysis of Distributed Avionics Systems
This paper presents a modeling framework for schedulability analysis of
distributed integrated modular avionics (DIMA) systems that consist of
spatially distributed ARINC-653 modules connected by a unified AFDX network. We
model a DIMA system as a set of stopwatch automata (SWA) in UPPAAL to analyze
its schedulability by classical model checking (MC) and statistical model
checking (SMC). The framework has been designed to enable three types of
analysis: global SMC, global MC, and compositional MC. This allows an effective
methodology including (1) quick schedulability falsification using global SMC
analysis, (2) direct schedulability proofs using global MC analysis in simple
cases, and (3) strict schedulability proofs using compositional MC analysis for
larger state space. The framework is applied to the analysis of a concrete DIMA
system.Comment: In Proceedings MARS/VPT 2018, arXiv:1803.0866
Statistical Model Checking for Stochastic Hybrid Systems
This paper presents novel extensions and applications of the UPPAAL-SMC model
checker. The extensions allow for statistical model checking of stochastic
hybrid systems. We show how our race-based stochastic semantics extends to
networks of hybrid systems, and indicate the integration technique applied for
implementing this semantics in the UPPAAL-SMC simulation engine. We report on
two applications of the resulting tool-set coming from systems biology and
energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315
- …