2,758 research outputs found
Integration of Quality Attributes in Software Product Line Development
Different
approaches
for
building
modern
software
systems
in
complex
and
open
environments
have
been
proposed
in
the
last
few
years.
Some
efforts
try
to
apply
Software
Product
Line
(SPL)
approach
to
take
advantage
of
the
massive
reuse
for
producing
software
systems
that
share
a
common
set
of
features.
In
general
quality
assurance
is
a
crucial
activity
for
success
in
software
industry,
but
it
is
even
more
important
when
talking
about
Software
Product
Lines
since
the
intensive
reuse
of
assets
makes
the
quality
attributes
(a
measurable
physical
or
abstract
property
of
an
entity)
of
the
assets
to
be
transmitted
to
the
whole
SPL
scope.
However,
despite
the
importance
that
quality
has
in
software
product
line
development,
most
of
the
methodologies
being
applied
in
Software
Product
Line
Development
focus
only
on
managing
the
commonalities
and
variability
within
the
product
line
and
not
giving
support
to
the
non--Âż
functional
requirements
that
the
products
must
fit.
The
main
goal
of
this
master
final
work
is
to introduce
quality
attributes
in
early
stages
of
software
product
line
development
processes
by
means
of
the
definition
of
a
production
plan
that,
on
one
hand,
integrates
quality
as
an
additional
view
for
describing
the
extension
of
the
software
product
line
and,
on
the
other
hand
introduces
the
quality
attributes
as
a
decision
factor
during
product
configuration
and
when
selecting
among
design
alternatives.
Our
approach
has
been
defined
following
the
Model--Âż
Driven
Software
Development
paradigm.
Therefore
all
the
software
artifacts
defined
had
its
correspondent
metamodels
and
the
processes
defined
rely
on
automated
model
transformations.
Finally
in
order
to
illustrate
the
feasibility
of
the
approach
we
have
integrated
the
quality
view
in
an
SPL
example
in
the
context
of
safety
critical
embedded
systems
on
the
automotive
domain.González Huerta, J. (2011). Integration of Quality Attributes in Software Product Line Development. http://hdl.handle.net/10251/15835Archivo delegad
Software variability in service robotics
Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments. To this end, the software needs to be variable—to customize robots for different customers, hardware, and operating environments. However, variability adds substantial complexity and needs to be managed—yet, ad hoc practices prevail in the robotics domain, challenging effective software reuse, maintenance, and evolution. To improve the situation, we need to enhance our empirical understanding of variability in robotics. We present a multiple-case study on software variability in the vibrant and challenging domain of service robotics. We investigated drivers, practices, methods, and challenges of variability from industrial companies building service robots. We analyzed the state-of-the-practice and the state-of-the-art—the former via an experience report and eleven interviews with two service robotics companies; the latter via a systematic literature review. We triangulated from these sources, reporting observations with actionable recommendations for researchers, tool providers, and practitioners. We formulated hypotheses trying to explain our observations, and also compared the state-of-the-art from the literature with the-state-of-the-practice we observed in our cases. We learned that the level of abstraction in robotics software needs to be raised for simplifying variability management and software integration, while keeping a sufficient level of customization to boost efficiency and effectiveness in their robots’ operation. Planning and realizing variability for specific requirements and implementing robust abstractions permit robotic applications to operate robustly in dynamic environments, which are often only partially known and controllable. With this aim, our companies use a number of mechanisms, some of them based on formalisms used to specify robotic behavior, such as finite-state machines and behavior trees. To foster software reuse, the service robotics domain will greatly benefit from having software components—completely decoupled from hardware—with harmonized and standardized interfaces, and organized in an ecosystem shared among various companies
Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
Impact of product complexity on inventory levels
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.Includes bibliographical references (leaves 62-63).In this thesis we consider a manufacturing and distribution supply chain of a roll-based product whose width comes in 1-cm increments. We formulate a computer model subject to stochastic, inelastic demand to determine the relationship between width interval and finished goods inventory levels. Assuming that the supply chain operates with the same set of policies regardless of the width interval value, we illustrate that the value of risk pooling diminishes as the interval widens. Due to the presence of a counteracting effect, we also demonstrate that increasing the width interval does not always reduce the amount of inventory requirements. Lastly, we show that the supply chain can operate with lower inventory levels without compromising the service level by pushing the inventory down the chain.by Ying-Lai (Chandler) See [and] Jin Namkoong.M.Eng.in Logistic
Model driven product line engineering : core asset and process implications
Reuse is at the heart of major improvements in productivity and quality in Software Engineering. Both Model Driven Engineering (MDE) and Software Product Line Engineering (SPLE) are software development paradigms that promote reuse. Specifically, they promote systematic reuse and a departure from craftsmanship towards an industrialization of the software development process. MDE and SPLE have established their benefits separately. Their combination, here called Model Driven Product Line Engineering (MDPLE), gathers together the advantages of both.
Nevertheless, this blending requires MDE to be recasted in SPLE terms. This has implications on both the core assets and the software development process. The challenges are twofold: (i) models become central core assets from which products are obtained and (ii) the software development process needs to cater for the changes that SPLE and MDE introduce. This dissertation proposes a solution to the first challenge following a feature oriented approach, with an emphasis on reuse and early detection of inconsistencies. The second part is dedicated to assembly processes, a clear example of the complexity MDPLE introduces in software development processes. This work advocates for a new discipline inside the general software development process, i.e., the Assembly Plan Management, which raises the abstraction level and increases reuse in such processes. Different case studies illustrate the presented ideas.This work was hosted by the University of the Basque Country (Faculty
of Computer Sciences). The author enjoyed a doctoral grant from the
Basque Goverment under the “Researchers Training Program” during the
years 2005 to 2009. The work was was co-supported by the Spanish Ministry
of Education, and the European Social Fund under contracts WAPO
(TIN2005-05610) and MODELINE (TIN2008-06507-C02-01)
Derivation and consistency checking of models in early software product line engineering
Dissertação para obtenção do Grau de Doutor em
Engenharia InformáticaSoftware Product Line Engineering (SPLE) should offer the ability to express the derivation of product-specific assets, while checking for their consistency. The derivation of product-specific assets is possible using general-purpose programming languages in combination with techniques
such as conditional compilation and code generation. On the other hand, consistency checking can be achieved through consistency rules in the form of architectural and design guidelines, programming conventions and well-formedness rules. Current approaches present four shortcomings: (1)
focus on code derivation only, (2) ignore consistency problems between the variability model and other complementary specification models used in early SPLE, (3) force developers to learn new, difficult to master, languages to encode the derivation of assets, and (4) offer no tool support.
This dissertation presents solutions that contribute to tackle these four shortcomings. These solutions are integrated in the approach Derivation and Consistency Checking of models in early SPLE (DCC4SPL) and its corresponding tool support.
The two main components of our approach are the Variability Modelling Language for Requirements(VML4RE), a domain-specific language and derivation infrastructure, and the Variability Consistency Checker (VCC), a verification technique and tool. We validate DCC4SPL demonstrating that it is appropriate to find inconsistencies in early SPL model-based specifications and to specify the derivation of product-specific models.European Project AMPLE, contract IST-33710; Fundação para a Ciência e Tecnologia - SFRH/BD/46194/2008
Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance
Abstract The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a human-centered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education
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