2,544 research outputs found
Synthesizing Adaptive Test Strategies from Temporal Logic Specifications
Constructing good test cases is difficult and time-consuming, especially if
the system under test is still under development and its exact behavior is not
yet fixed. We propose a new approach to compute test strategies for reactive
systems from a given temporal logic specification using formal methods. The
computed strategies are guaranteed to reveal certain simple faults in every
realization of the specification and for every behavior of the uncontrollable
part of the system's environment. The proposed approach supports different
assumptions on occurrences of faults (ranging from a single transient fault to
a persistent fault) and by default aims at unveiling the weakest one. Based on
well-established hypotheses from fault-based testing, we argue that such tests
are also sensitive for more complex bugs. Since the specification may not
define the system behavior completely, we use reactive synthesis algorithms
with partial information. The computed strategies are adaptive test strategies
that react to behavior at runtime. We work out the underlying theory of
adaptive test strategy synthesis and present experiments for a safety-critical
component of a real-world satellite system. We demonstrate that our approach
can be applied to industrial specifications and that the synthesized test
strategies are capable of detecting bugs that are hard to detect with random
testing
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
10451 Abstracts Collection -- Runtime Verification, Diagnosis, Planning and Control for Autonomous Systems
From November 7 to 12, 2010, the Dagstuhl Seminar 10451 ``Runtime Verification, Diagnosis, Planning and Control for Autonomous Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, 35 participants presented their current
research and discussed ongoing work and open problems.
This document puts together abstracts of the presentations given during the seminar, and provides links to extended abstracts or full papers, if available
Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning
A runtime assurance system (RTA) for a given plant enables the exercise of an
untrusted or experimental controller while assuring safety with a backup (or
safety) controller. The relevant computational design problem is to create a
logic that assures safety by switching to the safety controller as needed,
while maximizing some performance criteria, such as the utilization of the
untrusted controller. Existing RTA design strategies are well-known to be
overly conservative and, in principle, can lead to safety violations. In this
paper, we formulate the optimal RTA design problem and present a new approach
for solving it. Our approach relies on reward shaping and reinforcement
learning. It can guarantee safety and leverage machine learning technologies
for scalability. We have implemented this algorithm and present experimental
results comparing our approach with state-of-the-art reachability and
simulation-based RTA approaches in a number of scenarios using aircraft models
in 3D space with complex safety requirements. Our approach can guarantee safety
while increasing utilization of the experimental controller over existing
approaches
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