65 research outputs found
Model-Driven Engineering in the Large: Refactoring Techniques for Models and Model Transformation Systems
Model-Driven Engineering (MDE) is a software engineering paradigm that
aims to increase the productivity of developers by raising the
abstraction level of software development. It envisions the use of
models as key artifacts during design, implementation and deployment.
From the recent arrival of MDE in large-scale industrial software
development – a trend we refer to as MDE in the large –, a set of
challenges emerges: First, models are now developed at distributed
locations, by teams of teams. In such highly collaborative settings, the
presence of large monolithic models gives rise to certain issues, such
as their proneness to editing conflicts. Second, in large-scale system
development, models are created using various domain-specific modeling
languages. Combining these models in a disciplined manner calls for
adequate modularization mechanisms. Third, the development of models is
handled systematically by expressing the involved operations using model
transformation rules. Such rules are often created by cloning, a
practice related to performance and maintainability issues.
In this thesis, we contribute three refactoring techniques, each aiming
to tackle one of these challenges. First, we propose a technique to
split a large monolithic model into a set of sub-models. The aim of this
technique is to enable a separation of concerns within models, promoting
a concern-based collaboration style: Collaborators operate on the
submodels relevant for their task at hand. Second, we suggest a
technique to encapsulate model components by introducing modular
interfaces in a set of related models. The goal of this technique is to
establish modularity in these models. Third, we introduce a refactoring
to merge a set of model transformation rules exhibiting a high degree of
similarity. The aim of this technique is to improve maintainability and
performance by eliminating the drawbacks associated with cloning. The
refactoring creates variability-based rules, a novel type of rule
allowing to capture variability by using annotations.
The refactoring techniques contributed in this work help to reduce the
manual effort during the refactoring of models and transformation rules
to a large extent. As indicated in a series of realistic case studies,
the output produced by the techniques is comparable or, in the case of
transformation rules, partly even preferable to the result of manual
refactoring, yielding a promising outlook on the applicability in
real-world settings
Noninvasive brain stimulation techniques can modulate cognitive processing
Recent methods that allow a noninvasive modulation of brain activity are able to modulate human cognitive behavior. Among these methods are transcranial electric stimulation and transcranial magnetic stimulation that both come in multiple variants. A property of both types of brain stimulation is that they modulate brain activity and in turn modulate cognitive behavior. Here, we describe the methods with their assumed neural mechanisms for readers from the economic and social sciences and little prior knowledge of these techniques. Our emphasis is on available protocols and experimental parameters to choose from when designing a study. We also review a selection of recent studies that have successfully applied them in the respective field. We provide short pointers to limitations that need to be considered and refer to the relevant papers where appropriate
Family-Based Fingerprint Analysis: A Position Paper
Thousands of vulnerabilities are reported on a monthly basis to security
repositories, such as the National Vulnerability Database. Among these
vulnerabilities, software misconfiguration is one of the top 10 security risks
for web applications. With this large influx of vulnerability reports, software
fingerprinting has become a highly desired capability to discover distinctive
and efficient signatures and recognize reportedly vulnerable software
implementations. Due to the exponential worst-case complexity of fingerprint
matching, designing more efficient methods for fingerprinting becomes highly
desirable, especially for variability-intensive systems where optional features
add another exponential factor to its analysis. This position paper presents
our vision of a framework that lifts model learning and family-based analysis
principles to software fingerprinting. In this framework, we propose unifying
databases of signatures into a featured finite state machine and using presence
conditions to specify whether and in which circumstances a given input-output
trace is observed. We believe feature-based signatures can aid performance
improvements by reducing the size of fingerprints under analysis.Comment: Paper published in the Proceedings A Journey from Process Algebra via
Timed Automata to Model Learning: Essays Dedicated to Frits Vaandrager on the
Occasion of His 60th Birthday 202
A Rapid Prototyping Language Workbench for Textual DSLs based on Xtext: Vision and Progress
Metamodel-based DSL development in language workbenches like Xtext allows
language engineers to focus more on metamodels and domain concepts rather than
grammar details. However, the grammar generated from metamodels often requires
manual modification, which can be tedious and time-consuming. Especially when
it comes to rapid prototyping and language evolution, the grammar will be
generated repeatedly, this means that language engineers need to repeat such
manual modification back and forth. Previous work introduced GrammarOptimizer,
which automatically improves the generated grammar using optimization rules.
However, the optimization rules need to be configured manually, which lacks
user-friendliness and convenience. In this paper, we present our vision for and
current progress towards a language workbench that integrates
GrammarOptimizer's grammar optimization rules to support rapid prototyping and
evolution of metamodel-based languages. It provides a visual configuration of
optimization rules and a real-time preview of the effects of grammar
optimization to address the limitations of GrammarOptimizer. Furthermore, it
supports the inference of a grammar based on examples from model instances and
offers a selection of language styles. These features aim to enhance the
automation level of metamodel-based DSL development with Xtext and assist
language engineers in iterative development and rapid prototyping. Our paper
discusses the potential and applications of this language workbench, as well as
how it fills the gaps in existing language workbenches.Comment: 6 pages, 3 figure
Robotics Software Engineering: A Perspective from the Service Robotics Domain
Robots that support humans by performing useful tasks (a.k.a., service
robots) are booming worldwide. In contrast to industrial robots, the
development of service robots comes with severe software engineering
challenges, since they require high levels of robustness and autonomy to
operate in highly heterogeneous environments. As a domain with critical safety
implications, service robotics faces a need for sound software development
practices. In this paper, we present the first large-scale empirical study to
assess the state of the art and practice of robotics software engineering. We
conducted 18 semi-structured interviews with industrial practitioners working
in 15 companies from 9 different countries and a survey with 156 respondents
(from 26 countries) from the robotics domain. Our results provide a
comprehensive picture of (i) the practices applied by robotics industrial and
academic practitioners, including processes, paradigms, languages, tools,
frameworks, and reuse practices, (ii) the distinguishing characteristics of
robotics software engineering, and (iii) recurrent challenges usually faced,
together with adopted solutions. The paper concludes by discussing
observations, derived hypotheses, and proposed actions for researchers and
practitioners.Comment: 11 pages + 1 page for references, 3 figures, 3 tables, in proceedings
of ESEC/FSE 202
Optical inter-site spin transfer probed by energy and spin-resolved transient absorption spectroscopy
Optically driven spin transport is the fastest and most efficient process to manipulate macroscopic magnetization as it does not rely on secondary mechanisms to dissipate angular momentum. In the present work, we show that such an optical inter-site spin transfer (OISTR) from Pt to Co emerges as a dominant mechanism governing the ultrafast magnetization dynamics of a CoPt alloy. To demonstrate this, we perform a joint theoretical and experimental investigation to determine the transient changes of the helicity dependent absorption in the extreme ultraviolet spectral range. We show that the helicity dependent absorption is directly related to changes of the transient spin-split density of states, allowing us to link the origin of OISTR to the available minority states above the Fermi level. This makes OISTR a general phenomenon in optical manipulation of multi-component magnetic systems. Optically driven spin transfer is the fastest process to manipulate magnetism. Here, the authors show that this process emerges as the dominant mechanism in femtosecond spin dynamics enabling to the engineering of functional magnetic systems for future all optical technologies
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