9,521 research outputs found
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
Engineering model transformations with transML
The final publication is available at Springer via http://dx.doi.org/10.1007%2Fs10270-011-0211-2Model transformation is one of the pillars of model-driven engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations as well. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. In this way, even though transformations should be built using sound engineering principles—just like any other kind of software—there is currently a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life cycle of transformation development enabling the engineering of transformations. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for several transformation implementation languages.This work has been sponsored by the Spanish Ministry of Science and Innovation with project METEORIC (TIN2008-02081), and by the R&D program of the Community of Madrid with projects “e-Madrid" (S2009/TIC-1650). Parts of this work were done during the research stays of Esther and Juan at the University of York, with financial support from the Spanish Ministry of Science and Innovation (grant refs. JC2009-00015, PR2009-0019 and PR2008-0185)
Potential Errors and Test Assessment in Software Product Line Engineering
Software product lines (SPL) are a method for the development of variant-rich
software systems. Compared to non-variable systems, testing SPLs is extensive
due to an increasingly amount of possible products. Different approaches exist
for testing SPLs, but there is less research for assessing the quality of these
tests by means of error detection capability. Such test assessment is based on
error injection into correct version of the system under test. However to our
knowledge, potential errors in SPL engineering have never been systematically
identified before. This article presents an overview over existing paradigms
for specifying software product lines and the errors that can occur during the
respective specification processes. For assessment of test quality, we leverage
mutation testing techniques to SPL engineering and implement the identified
errors as mutation operators. This allows us to run existing tests against
defective products for the purpose of test assessment. From the results, we
draw conclusions about the error-proneness of the surveyed SPL design paradigms
and how quality of SPL tests can be improved.Comment: In Proceedings MBT 2015, arXiv:1504.0192
S2ST: A Relational RDF Database Management System
The explosive growth of RDF data on the Semantic Web drives the need for novel database systems that can efficiently store and query large RDF datasets. To achieve good performance and scalability of query processing, most existing RDF storage systems use a relational database management system as a backend to manage RDF data. In this paper, we describe the design and implementation of a Relational RDF Database Management System. Our main research contributions are: (1) We propose a formal model of a Relational RDF Database Management System (RRDBMS), (2) We propose generic algorithms for schema, data and query mapping, (3) We implement the first and only RRDBMS, S2ST, that supports multiple relational database management systems, user-customizable schema mapping, schema-independent data mapping, and semantics-preserving query translation
Engineering Agile Big-Data Systems
To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems
The ModelCC Model-Driven Parser Generator
Syntax-directed translation tools require the specification of a language by
means of a formal grammar. This grammar must conform to the specific
requirements of the parser generator to be used. This grammar is then annotated
with semantic actions for the resulting system to perform its desired function.
In this paper, we introduce ModelCC, a model-based parser generator that
decouples language specification from language processing, avoiding some of the
problems caused by grammar-driven parser generators. ModelCC receives a
conceptual model as input, along with constraints that annotate it. It is then
able to create a parser for the desired textual syntax and the generated parser
fully automates the instantiation of the language conceptual model. ModelCC
also includes a reference resolution mechanism so that ModelCC is able to
instantiate abstract syntax graphs, rather than mere abstract syntax trees.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
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