41,876 research outputs found
A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization
UFO is a new implementation of FORMAN, a declarative monitoring language, in
which rules are compiled into execution monitors that run on a virtual machine
supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth
International Workshop on Automated Debugging (AADEBUG 2003), September 2003,
Ghent. cs.SE/030902
Extracting Formal Models from Normative Texts
We are concerned with the analysis of normative texts - documents based on
the deontic notions of obligation, permission, and prohibition. Our goal is to
make queries about these notions and verify that a text satisfies certain
properties concerning causality of actions and timing constraints. This
requires taking the original text and building a representation (model) of it
in a formal language, in our case the C-O Diagram formalism. We present an
experimental, semi-automatic aid that helps to bridge the gap between a
normative text in natural language and its C-O Diagram representation. Our
approach consists of using dependency structures obtained from the
state-of-the-art Stanford Parser, and applying our own rules and heuristics in
order to extract the relevant components. The result is a tabular data
structure where each sentence is split into suitable fields, which can then be
converted into a C-O Diagram. The process is not fully automatic however, and
some post-editing is generally required of the user. We apply our tool and
perform experiments on documents from different domains, and report an initial
evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International
Conference on Applications of Natural Language to Information Systems (NLDB
2016). arXiv admin note: substantial text overlap with arXiv:1607.0148
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training.Comment: 9 pages, 7 figure
Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model
To harness the power of multi-core and distributed platforms, and to make the
development of concurrent software more accessible to software engineers,
different object-oriented concurrency models such as SCOOP have been proposed.
Despite the practical importance of analysing SCOOP programs, there are
currently no general verification approaches that operate directly on program
code without additional annotations. One reason for this is the multitude of
partially conflicting semantic formalisations for SCOOP (either in theory or
by-implementation). Here, we propose a simple graph transformation system (GTS)
based run-time semantics for SCOOP that grasps the most common features of all
known semantics of the language. This run-time model is implemented in the
state-of-the-art GTS tool GROOVE, which allows us to simulate, analyse, and
verify a subset of SCOOP programs with respect to deadlocks and other
behavioural properties. Besides proposing the first approach to verify SCOOP
programs by automatic translation to GTS, we also highlight our experiences of
applying GTS (and especially GROOVE) for specifying semantics in the form of a
run-time model, which should be transferable to GTS models for other concurrent
languages and libraries.Comment: In Proceedings GaM 2015, arXiv:1504.0244
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