11,359 research outputs found
Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata
To analyze complex and heterogeneous real-time embedded systems, recent works
have proposed interface techniques between real-time calculus (RTC) and timed
automata (TA), in order to take advantage of the strengths of each technique
for analyzing various components. But the time to analyze a state-based
component modeled by TA may be prohibitively high, due to the state space
explosion problem. In this paper, we propose a framework of granularity-based
interfacing to speed up the analysis of a TA modeled component. First, we
abstract fine models to work with event streams at coarse granularity. We
perform analysis of the component at multiple coarse granularities and then
based on RTC theory, we derive lower and upper bounds on arrival patterns of
the fine output streams using the causality closure algorithm. Our framework
can help to achieve tradeoffs between precision and analysis time.Comment: QAPL 201
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Compositional Verification for Autonomous Systems with Deep Learning Components
As autonomy becomes prevalent in many applications, ranging from
recommendation systems to fully autonomous vehicles, there is an increased need
to provide safety guarantees for such systems. The problem is difficult, as
these are large, complex systems which operate in uncertain environments,
requiring data-driven machine-learning components. However, learning techniques
such as Deep Neural Networks, widely used today, are inherently unpredictable
and lack the theoretical foundations to provide strong assurance guarantees. We
present a compositional approach for the scalable, formal verification of
autonomous systems that contain Deep Neural Network components. The approach
uses assume-guarantee reasoning whereby {\em contracts}, encoding the
input-output behavior of individual components, allow the designer to model and
incorporate the behavior of the learning-enabled components working
side-by-side with the other components. We illustrate the approach on an
example taken from the autonomous vehicles domain
Graphical modelling language for spycifying concurrency based on CSP
Introduced in this (shortened) paper is a graphical modelling language for specifying concurrency in software designs. The language notations are derived from CSP and the resulting designs form CSP diagrams. The notations reflect both data-flow and control-flow aspects of concurrent software architectures. These designs can automatically be described by CSP algebraic expressions that can be used for formal analysis. The designer does not have to be aware of the underlying mathematics. The techniques and rules presented provide guidance to the development of concurrent software architectures. One can detect and reason about compositional conflicts (errors in design), potential deadlocks (errors at run-time), and priority inversion problems (performance burden) at a high level of abstraction. The CSP diagram collaborates with objectoriented modelling languages and structured methods
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