7,113 research outputs found
Evaluating the performance of model transformation styles in Maude
Rule-based programming has been shown to be very successful in many application areas. Two prominent examples are the specification of model transformations in model driven development approaches and the definition of structured operational semantics of formal languages. General rewriting frameworks such as Maude are flexible enough to allow the programmer to adopt and mix various rule styles. The choice between styles can be biased by the programmer’s background. For instance, experts in visual formalisms might prefer graph-rewriting styles, while experts in semantics might prefer structurally inductive rules. This paper evaluates the performance of different rule styles on a significant benchmark taken from the literature on model transformation. Depending on the actual transformation being carried out, our results show that different rule styles can offer drastically different performances. We point out the situations from which each rule style benefits to offer a valuable set of hints for choosing one style over the other
Evaluation of Model Transformation Approaches for Model Refactoring
This paper provides a systematic evaluation framework for comparing
model transformation approaches, based upon the ISO/IEC 9126-1
quality characteristics for software systems. We apply this framework to
compare five transformation approaches (QVT-R, ATL, Kermeta, UMLRSDS
and GrGen.NET) on a complex model refactoring case study: the
amalgamation of apparent attribute clones in a class diagram.
The case study highlights the problems with the specification and design
of the refactoring category of model transformations, and provides
a challenging example by which model transformation languages and approaches
can be compared. We take into account a wide range of evaluation
criteria aspects such as correctness, efficiency, flexibility, interoperability,
reusability and robustness, which have not been comprehensively
covered by other comparative surveys of transformation approaches.
The results show clear distinctions between the capabilities and suitabilities
of different approaches to address the refactoring form of transformation
problem
Programmable Insight: A Computational Methodology to Explore Online News Use of Frames
abstract: The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Proceedings of the Weizenbaum Conference 2023: AI, Big Data, Social Media, and People on the Move
The conference focused on topics that arise from artificial intelligence (AI) and Big Data deployed on and used by 'people on the move'. We understand the term 'people on the move' in a broad sense: individuals and groups who - by volition or necessity - are changing their lives and/or their structural position in societies. This encompasses the role of automated systems or AI in different forms of geographical and social change, including migration and labour mobility, algorithmic uses of 'location', as well as discourses of and about people on the move
Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling
Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)
automatic approaches to derive model transformations from meta-models or from
examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex
transformations. We propose a novel unsupervised approach, called Ockham, which
is able to learn edit operations from model histories in model repositories. Ockham
is based on the idea that meaningful domain-specifc edit operations are the ones
that compress the model diferences. It employs frequent subgraph mining to discover frequent structures in model diference graphs. We evaluate our approach in
two controlled experiments and one real-world case study of a large-scale industrial
model-driven architecture project in the railway domain. We found that our approach
is able to discover frequent edit operations that have actually been applied before.
Furthermore, Ockham is able to extract edit operations that are meaningful—in the
sense of explaining model diferences through the edit operations they comprise—to
practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model diferences and change profles) for the discovered edit operations in
this industrial setting. We fnd that the edit operations discovered by Ockham can be
used to better understand and simulate the evolution of models
A local and global tour on MOMoT
Many model transformation scenarios require flexible execution strategies as they should produce models with the highest
possible quality. At the same time, transformation problems often span a very large search space with respect to possible
transformation results. Recently, different proposals for finding good transformation results without enumerating the
complete search space have been proposed by using meta-heuristic search algorithms. However, determining the impact of
the different kinds of search algorithms, such as local search or global search, on the transformation results is still an open
research topic. In this paper, we present an extension to MOMoT, which is a search-based model transformation tool, for
supporting not only global searchers for model transformation orchestrations, but also local ones. This leads to a model
transformation framework that allows as the first of its kind multi-objective local and global search. By this, the advantages
and disadvantages of global and local search for model transformation orchestration can be evaluated. This is done in a
case-study-based evaluation, which compares different performance aspects of the local- and global-search algorithms
available in MOMoT. Several interesting conclusions have been drawn from the evaluation: (1) local-search algorithms
perform reasonable well with respect to both the search exploration and the execution time for small input models, (2) for
bigger input models, their execution time can be similar to those of global-search algorithms, but global-search algorithms
tend to outperform local-search algorithms in terms of search exploration, (3) evolutionary algorithms show limitations in
situations where single changes of the solution can have a significant impact on the solution’s fitness.Ministerio de Economia y Competitividad TIN2015-70560-RJunta de Andalucía P12-TIC-186
Model Transformation Languages with Modular Information Hiding
Model transformations, together with models, form the principal artifacts in model-driven software development. Industrial practitioners report that transformations on larger models quickly get sufficiently large and complex themselves. To alleviate entailed maintenance efforts, this thesis presents a modularity concept with explicit interfaces, complemented by software visualization and clustering techniques. All three approaches are tailored to the specific needs of the transformation domain
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