37,371 research outputs found
Towards Product Lining Model-Driven Development Code Generators
A code generator systematically transforms compact models to detailed code.
Today, code generation is regarded as an integral part of model-driven
development (MDD). Despite its relevance, the development of code generators is
an inherently complex task and common methodologies and architectures are
lacking. Additionally, reuse and extension of existing code generators only
exist on individual parts. A systematic development and reuse based on a code
generator product line is still in its infancy. Thus, the aim of this paper is
to identify the mechanism necessary for a code generator product line by (a)
analyzing the common product line development approach and (b) mapping those to
a code generator specific infrastructure. As a first step towards realizing a
code generator product line infrastructure, we present a component-based
implementation approach based on ideas of variability-aware module systems and
point out further research challenges.Comment: 6 pages, 1 figure, Proceedings of the 3rd International Conference on
Model-Driven Engineering and Software Development, pp. 539-545, Angers,
France, SciTePress, 201
Integrating the common variability language with multilanguage annotations for web engineering
Web applications development involves managing a high diversity of files and resources like code, pages or style sheets, implemented in different languages. To deal with the automatic generation of
custom-made configurations of web applications, industry usually adopts annotation-based approaches even though the majority of studies encourage the use of composition-based approaches to implement
Software Product Lines. Recent work tries to combine both approaches to get the complementary benefits. However, technological companies are reticent to adopt new development paradigms
such as feature-oriented programming or aspect-oriented programming.
Moreover, it is extremely difficult, or even impossible, to apply
these programming models to web applications, mainly because of
their multilingual nature, since their development involves multiple
types of source code (Java, Groovy, JavaScript), templates (HTML,
Markdown, XML), style sheet files (CSS and its variants, such as
SCSS), and other files (JSON, YML, shell scripts). We propose to
use the Common Variability Language as a composition-based approach
and integrate annotations to manage fine grained variability
of a Software Product Line for web applications. In this paper, we (i)
show that existing composition and annotation-based approaches,
including some well-known combinations, are not appropriate to
model and implement the variability of web applications; and (ii)
present a combined approach that effectively integrates annotations
into a composition-based approach for web applications. We implement
our approach and show its applicability with an industrial
real-world system.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Using Feature Models for Distributed Deployment in Extended Smart Home Architecture
Nowadays, smart home is extended beyond the house itself to encompass
connected platforms on the Cloud as well as mobile personal devices. This Smart
Home Extended Architecture (SHEA) helps customers to remain in touch with their
home everywhere and any time. The endless increase of connected devices in the
home and outside within the SHEA multiplies the deployment possibilities for
any application. Therefore, SHEA should be taken from now as the actual target
platform for smart home application deployment. Every home is different and
applications offer different services according to customer preferences. To
manage this variability, we extend the feature modeling from software product
line domain with deployment constraints and we present an example of a model
that could address this deployment challenge
Variability Abstractions: Trading Precision for Speed in Family-Based Analyses (Extended Version)
Family-based (lifted) data-flow analysis for Software Product Lines (SPLs) is
capable of analyzing all valid products (variants) without generating any of
them explicitly. It takes as input only the common code base, which encodes all
variants of a SPL, and produces analysis results corresponding to all variants.
However, the computational cost of the lifted analysis still depends inherently
on the number of variants (which is exponential in the number of features, in
the worst case). For a large number of features, the lifted analysis may be too
costly or even infeasible. In this paper, we introduce variability abstractions
defined as Galois connections and use abstract interpretation as a formal
method for the calculational-based derivation of approximate (abstracted)
lifted analyses of SPL programs, which are sound by construction. Moreover,
given an abstraction we define a syntactic transformation that translates any
SPL program into an abstracted version of it, such that the analysis of the
abstracted SPL coincides with the corresponding abstracted analysis of the
original SPL. We implement the transformation in a tool, reconfigurator that
works on Object-Oriented Java program families, and evaluate the practicality
of this approach on three Java SPL benchmarks.Comment: 50 pages, 10 figure
Feature-Aware Verification
A software product line is a set of software products that are distinguished
in terms of features (i.e., end-user--visible units of behavior). Feature
interactions ---situations in which the combination of features leads to
emergent and possibly critical behavior--- are a major source of failures in
software product lines. We explore how feature-aware verification can improve
the automatic detection of feature interactions in software product lines.
Feature-aware verification uses product-line verification techniques and
supports the specification of feature properties along with the features in
separate and composable units. It integrates the technique of variability
encoding to verify a product line without generating and checking a possibly
exponential number of feature combinations. We developed the tool suite
SPLverifier for feature-aware verification, which is based on standard
model-checking technology. We applied it to an e-mail system that incorporates
domain knowledge of AT&T. We found that feature interactions can be detected
automatically based on specifications that have only feature-local knowledge,
and that variability encoding significantly improves the verification
performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl
A heuristic-based approach to code-smell detection
Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache
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