162,991 research outputs found
A Method for Developing Model to Text Transformations
In the field of business process development, model transformations play a key role, for example for moving from business process models to either code or inputs for simulation systems, as well as to convert models expressed with notation A into equivalent models expressed with notation B. In the literature, many cases of useful transformations of business process models can be found. However, in general each transformation has been developed in an ad-hoc fashion, at a quite low-level, and its quality is often neglected. To ensure the quality of the transformations is important to apply to them all the well-known software engineering principles and practices, from the requirements definition to the testing activities. For this reason, we propose a method, MeDMoT, for developing non-trivial Model to Text Transformations, which prescribes how to: (1) capture and specify the transformation requirements; (2) design the transformation, (3) implement the transformation and (4) test the transformation. The method has been applied in several case studies, including a transformation of UML business processes into inputs for an agent-based simulator
A Model-Driven approach for functional test case generation
Test phase is one of the most critical phases in software engineering life cycle to assure the final system quality. In this context, functional system test cases verify that the system under test fulfills its functional specification. Thus, these test cases are frequently designed from the different scenarios and alternatives depicted in functional requirements. The objective of this paper is to introduce a systematic process based on the Model-Driven paradigm to automate the generation of functional test cases from functional requirements. For this aim, a set of metamodels and transformations and also a specific language domain to use them is presented. The paper finishes stating learned lessons from the trenches as well as relevant future work and conclusions that draw new research lines in the test cases generation context.Ministerio de EconomÃa y Competitividad TIN2013-46928-C3-3-
Testing M2T/T2M Transformations
Presentado en: 16th International Conference on Model Driven Engineering Languages and Systems (MODELS 2013). Del 29 de septiembre al 4 de octubre. Miami, EEUU.Testing model-to-model (M2M) transformations is becoming a prominent topic in the current Model-driven Engineering landscape. Current approaches for transformation testing, however, assume having explicit model representations for the input domain and for the output domain of the transformation. This excludes other important transformation kinds, such as model-to-text (M2T) and text-to-model (T2M) transformations, from being properly tested since adequate model representations are missing either for the input domain or for the output domain. The contribution of this paper to overcome this gap is extending Tracts, a M2M transformation testing approach, for M2T/T2M transformation testing. The main mechanism we employ for reusing Tracts is to represent text within a generic metamodel. By this, we transform the M2T/T2M transformation specification problems into equivalent M2M transformation specification problems. We demonstrate the applicability of the approach by two examples and present how the approach is implemented for the Eclipse Modeling Framework (EMF). Finally, we apply the approach to evaluate code generation capabilities of several existing UML tools.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech. Proyecto TIN2011-2379
Improving NDT with Automatic Test Case Generation
The model-driven development defi nes the software development process as a set of
iterations to create models and a set of transformations to obtain new models. From
this point of view, this paper presents the enhancement of a model- driven approach,
called navigational development techniques (NDT), by means of new models and
transformations in order to generate test cases. It also states some conclusions from
the research work and practical cases in which this approach was used.Ministerio de Ciencia e Innovación TIN2010-20057-C03-02Ministerio de Ciencia e Innovación TIN 2010-12312-
On the Generalization Effects of Linear Transformations in Data Augmentation
Data augmentation is a powerful technique to improve performance in
applications such as image and text classification tasks. Yet, there is little
rigorous understanding of why and how various augmentations work. In this work,
we consider a family of linear transformations and study their effects on the
ridge estimator in an over-parametrized linear regression setting. First, we
show that transformations which preserve the labels of the data can improve
estimation by enlarging the span of the training data. Second, we show that
transformations which mix data can improve estimation by playing a
regularization effect. Finally, we validate our theoretical insights on MNIST.
Based on the insights, we propose an augmentation scheme that searches over the
space of transformations by how uncertain the model is about the transformed
data. We validate our proposed scheme on image and text datasets. For example,
our method outperforms RandAugment by 1.24% on CIFAR-100 using
Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA
Adversarial AutoAugment on CIFAR datasets.Comment: International Conference on Machine learning (ICML) 2020. Added
experimental results on ImageNe
A Model-Derivation Framework for Software Analysis
Model-based verification allows to express behavioral correctness conditions
like the validity of execution states, boundaries of variables or timing at a
high level of abstraction and affirm that they are satisfied by a software
system. However, this requires expressive models which are difficult and
cumbersome to create and maintain by hand. This paper presents a framework that
automatically derives behavioral models from real-sized Java programs. Our
framework builds on the EMF/ECore technology and provides a tool that creates
an initial model from Java bytecode, as well as a series of transformations
that simplify the model and eventually output a timed-automata model that can
be processed by a model checker such as UPPAAL. The framework has the following
properties: (1) consistency of models with software, (2) extensibility of the
model derivation process, (3) scalability and (4) expressiveness of models. We
report several case studies to validate how our framework satisfies these
properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581
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