scholarly journals Assessing and improving quality of QVTo model transformations

2015 ◽  
Vol 24 (3) ◽  
pp. 797-834 ◽  
Author(s):  
Christine M. Gerpheide ◽  
Ramon R. H. Schiffelers ◽  
Alexander Serebrenik
Author(s):  
Marcel F. van Amstel ◽  
Christian F. J. Lange ◽  
Mark G. J. van den Brand

2011 ◽  
Vol 8 (1) ◽  
pp. 41-72
Author(s):  
Peter Kajsa ◽  
Lubomir Majtas ◽  
Pavol Navrat

Design patterns provide an especially effective way to improve the quality of a software system design as they provide abstracted, generalized and verified solutions of non-trivial design problems that occur repeatedly. The paper presents a method of design pattern instantiation support based on the key principles of both MDD and MDA. The method allows specification of the pattern instance occurrence via the semantic extension of UML directly on the context. The rest of the pattern instantiation is automated by model transformations of the specified pattern instances to lower levels of abstraction. Such approach enables the use of higher levels of abstraction in the modeling of patterns. Moreover, the model transformations are driven by models of patterns besides the instance specification, and thus the approach provides very useful ways how to determine and control the results of transformations. The method is not limited to design pattern support only, it also provides a framework for the addition of support for custom model structures which are often created in models mechanically.


Author(s):  
Justinas Janulevicius ◽  
Simona Ramanauskaite ◽  
Nikolaj Goranin ◽  
Antanas Cenys

Model-Driven Engineering uses models in various stages of the software engineering. To reduce the cost of modelling and production, models are reused by transforming. Therefore the accuracy of model transformations plays a key role in ensuring the quality of the process. However, problems exist when trying to transform a very abstract and content dependent model. This paper describes the issues arising from such transformations. Solutions to solve problems in content based model transformation are proposed as well. The usage of proposed solutions allowing realization of semi-automatic transformations was integrated into a tool, designed for OPC/XML drawing file transformations to CySeMoL models. The accuracy of transformations in this tool has been analyzed and presented in this paper to acquire data on the proposed solutions influence to the accuracy in content based model transformation.


2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Beatriz Marín ◽  
Giovanni Giachetti ◽  
Oscar Pastor ◽  
Alain Abran

In Model-Driven Development (MDD) processes, models are key artifacts that are used as input for code generation. Therefore, it is very important to evaluate the quality of these input models in order to obtain high-quality software products. The detection of defects is a promising technique to evaluate software quality, which is emerging as a suitable alternative for MDD processes. The detection of defects in conceptual models is usually manually performed. However, since current MDD standards and technologies allow both the specification of metamodels to represent conceptual models and the implementation of model transformations to automate the generation of final software products, it is possible to automate defect detection from the defined conceptual models. This paper presents a quality model that not only encapsulates defect types that are related to conceptual models but also takes advantage of current standards in order to automate defect detection in MDD environments.


2020 ◽  
Author(s):  
Jonathan Warrell ◽  
Hussein Mohsen ◽  
Mark Gerstein

AbstractDeep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide a general approach to interpreting a trained network post-hoc: the network is approximated by a model, which is typically compressed, whose structure can be more easily interpreted in some way (we call such approaches interpretability schemes). However, the relationship between compression and interpretation has not been fully explored: How much should a network be compressed for optimal extraction of interpretable information? Should compression be combined with other criteria when selecting model transformations? We investigate these issues using two different compression-based schemes, which aim to extract orthogonal kinds of information, pertaining to feature and data instance-based groupings respectively. The first (rank projection trees) uses a structured sparsification method such that nested groups of features can be extracted having potential joint interactions. The second (cascaded network decomposition) splits a network into a cascade of simpler networks, allowing groups of training instances with similar characteristics to be extracted at each stage of the cascade. We use predictive tasks in cancer and psychiatric genomics to assess the ability of these approaches to extract informative feature and data-point groupings from trained networks. We show that the generalization error of a network provides an indicator of the quality of the information extracted; further we derive PAC-Bayes generalization bounds for both schemes, which we show can be used as proxy indicators, and can thus provide a criterion for selecting the optimal compression. Finally, we show that the PAC-Bayes framework can be naturally modified to incorporate additional criteria alongside compression, such as prior knowledge based on previous models, which can enhance interpretable model selection.


Author(s):  
K. T. Tokuyasu

During the past investigations of immunoferritin localization of intracellular antigens in ultrathin frozen sections, we found that the degree of negative staining required to delineate u1trastructural details was often too dense for the recognition of ferritin particles. The quality of positive staining of ultrathin frozen sections, on the other hand, has generally been far inferior to that attainable in conventional plastic embedded sections, particularly in the definition of membranes. As we discussed before, a main cause of this difficulty seemed to be the vulnerability of frozen sections to the damaging effects of air-water surface tension at the time of drying of the sections.Indeed, we found that the quality of positive staining is greatly improved when positively stained frozen sections are protected against the effects of surface tension by embedding them in thin layers of mechanically stable materials at the time of drying (unpublished).


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