102,482 research outputs found
A model-driven approach to broaden the detection of software performance antipatterns at runtime
Performance antipatterns document bad design patterns that have negative
influence on system performance. In our previous work we formalized such
antipatterns as logical predicates that predicate on four views: (i) the static
view that captures the software elements (e.g. classes, components) and the
static relationships among them; (ii) the dynamic view that represents the
interaction (e.g. messages) that occurs between the software entities elements
to provide the system functionalities; (iii) the deployment view that describes
the hardware elements (e.g. processing nodes) and the mapping of the software
entities onto the hardware platform; (iv) the performance view that collects
specific performance indices. In this paper we present a lightweight
infrastructure that is able to detect performance antipatterns at runtime
through monitoring. The proposed approach precalculates such predicates and
identifies antipatterns whose static, dynamic and deployment sub-predicates are
validated by the current system configuration and brings at runtime the
verification of performance sub-predicates. The proposed infrastructure
leverages model-driven techniques to generate probes for monitoring the
performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043
A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data
In this paper we address the problem of matching patterns in the so-called
verification setting in which a novel, query pattern is verified against a
single training pattern: the decision sought is whether the two match (i.e.
belong to the same class) or not. Unlike previous work which has universally
focused on the development of more discriminative distance functions between
patterns, here we consider the equally important and pervasive task of
selecting a distance threshold which fits a particular operational requirement
- specifically, the target false positive rate (FPR). First, we argue on
theoretical grounds that a data-driven approach is inherently ill-conditioned
when the desired FPR is low, because by the very nature of the challenge only a
small portion of training data affects or is affected by the desired threshold.
This leads us to propose a general, statistical model-based method instead. Our
approach is based on the interpretation of an inter-pattern distance as
implicitly defining a pattern embedding which approximately distributes
patterns according to an isotropic multi-variate normal distribution in some
space. This interpretation is then used to show that the distribution of
training inter-pattern distances is the non-central chi2 distribution,
differently parameterized for each class. Thus, to make the class-specific
threshold choice we propose a novel analysis-by-synthesis iterative algorithm
which estimates the three free parameters of the model (for each class) using
task-specific constraints. The validity of the premises of our work and the
effectiveness of the proposed method are demonstrated by applying the method to
the task of set-based face verification on a large database of pseudo-random
head motion videos.Comment: IEEE/IAPR International Joint Conference on Biometrics, 201
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Integrated design for integrated photonics: from the physical to the circuit level and back
Silicon photonics is maturing rapidly on a technology basis, but design challenges are still prevalent. We discuss these challenges and explain how design of photonic integrated circuits needs to be handled on both the circuit as on the physical level. We also present a number of tools based on the IPKISS design framework
Towards a Formal Framework for Mobile, Service-Oriented Sensor-Actuator Networks
Service-oriented sensor-actuator networks (SOSANETs) are deployed in
health-critical applications like patient monitoring and have to fulfill strong
safety requirements. However, a framework for the rigorous formal modeling and
analysis of SOSANETs does not exist. In particular, there is currently no
support for the verification of correct network behavior after node failure or
loss/addition of communication links. To overcome this problem, we propose a
formal framework for SOSANETs. The main idea is to base our framework on the
\pi-calculus, a formally defined, compositional and well-established formalism.
We choose KLAIM, an existing formal language based on the \pi-calculus as the
foundation for our framework. With that, we are able to formally model SOSANETs
with possible topology changes and network failures. This provides the basis
for our future work on prediction, analysis and verification of the network
behavior of these systems. Furthermore, we illustrate the real-life
applicability of this approach by modeling and extending a use case scenario
from the medical domain.Comment: In Proceedings FESCA 2013, arXiv:1302.478
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