23 research outputs found
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
Architecture design studio pedagogy for translating environmental sustainable elements
Sustainable design helps reduce negative impacts on the environment and improve building performance. The architectural educators strive to impart the sustainable requisite to students. Based on the literature review and the results of an exploratory study conducted, it is evident that the pedagogy employed by Universiti Teknologi Malaysia (UTM) architectural educators follows reflective-in-action and Kolbâs theory. However, the environmental sustainable design elements are not reflected in most architectural design studio curriculum. In fact, only a few courses have elements of environmental sustainable design embedded in them. This research aims to determine the manner in which architectural educators in UTM translate environmental sustainable design elements to students. A mixed method was employed in this study: observation on the second year environmental design studio was done for four (4) months (n=7); a questionnaire was distributed to all architectural students (n=150), and interviews of educators (n=17) involved in workbase studios in the department of Architecture were conducted. The data from the observation was analyzed with categorical data analysis with a percent agreement set at 70% inter-coder reliability coefficient. The questionnaire was analyzed using SPSS version 20, with a one way ANOVA set at p<0.05 significance level to obtain results for inferences, while the interviews were analyzed by content analysis. Results on the analysis show that the architectural educators imparted aspect of environmental sustainable design elements directly to the students through various pedagogies, and the students used those environmental sustainable design elements in their design studio work. The results also reveal that the architectural curriculum is a hidden curriculum which embeds sustainable design elements; however, understanding of building ecosystem and ability to design sustainable buildings are not enforced on the students across all the design studios. It is only mandatory in the second semester of the second year studio since the theme is on the environmental paradigm. This implies that in order to empower students with the ability to design environmental sustainable buildings, more sustainable core subjects could be included in the studio curriculum. Findings could be employed by architectural educators and policy makers as a guide for future curriculum upgrading and development
Human face detection techniques: A comprehensive review and future research directions
Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection
Cyber-storms come from clouds:Security of cloud computing in the IoT era
The Internet of Things (IoT) is rapidly changing our society to a world where every âthingâ is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds
Visualizing defects in source code
Trabalho de investigação desenvolvido na Cranfield University. School of EngineeringTese de mestrado integrado. Engenharia Informåtica e Computação. Faculdade de Engenharia. Universidade do Porto. 201
A Scholarship Approach to Model-Driven Engineering
Model-Driven Engineering is a paradigm for software engineering where software models are the primary artefacts throughout the software life-cycle. The aim is to define suitable representations and processes that enable precise and efficient specification, development and analysis of software. Our contributions to Model-Driven Engineering are structured according to Boyer\u27s four functions of academic activity - the scholarships of teaching, discovery, application and integration. The scholarships share a systematic approach towards seeking new insights and promoting progressive change. Even if the scholarships have their differences they are compatible so that theory, practice and teaching can strengthen each other.Scholarship of Teaching: While teaching Model-Driven Engineering to under-graduate students we introduced two changes to our course. The first change was to introduce a new modelling tool that enabled the execution of software models while the second change was to adapt pair lecturing to encourage the students to actively participate in developing models during lectures. Scholarship of Discovery: By using an existing technology for transforming models into source code we translated class diagrams and high-level action languages into natural language texts. The benefit of our approach is that the translations are applicable to a family of models while the texts are reusable across different low-level representations of the same model.Scholarship of Application: Raising the level of abstraction through models might seem a technical issue but our collaboration with industry details how the success of adopting Model-Driven Engineering depends on organisational and social factors as well as technical. Scholarship of Integration: Building on our insights from the scholarships above and a study at three large companies we show how Model-Driven Engineering empowers new user groups to become software developers but also how engineers can feel isolated due to poor tool support. Our contributions also detail how modelling enables a more agile development process as well as how the validation of models can be facilitated through text generation.The four scholarships allow for different possibilities for insights and explore Model-Driven Engineering from diverse perspectives. As a consequence, we investigate the social, organisational and technological factors of Model-Driven Engineering but also examine the possibilities and challenges of Model-Driven Engineering across disciplines and scholarships
Pattern-based refactoring in model-driven engineering
LâingĂ©nierie dirigĂ©e par les modĂšles (IDM) est un paradigme du gĂ©nie logiciel qui utilise les
modĂšles comme concepts de premier ordre Ă partir desquels la validation, le code, les tests
et la documentation sont dérivés. Ce paradigme met en jeu divers artefacts tels que les
modÚles, les méta-modÚles ou les programmes de transformation des modÚles. Dans un
contexte industriel, ces artefacts sont de plus en plus complexes. En particulier, leur
maintenance demande beaucoup de temps et de ressources. Afin de réduire la complexité
des artefacts et le coût de leur maintenance, de nombreux chercheurs se sont intéressés au
refactoring de ces artefacts pour améliorer leur qualité.
Dans cette thĂšse, nous proposons dâĂ©tudier le refactoring dans lâIDM dans sa
globalité, par son application à ces différents artefacts. Dans un premier temps, nous
utilisons des patrons de conception spécifiques, comme une connaissance a priori, appliqués
aux transformations de modÚles comme un véhicule pour le refactoring. Nous procédons
dâabord par une phase de dĂ©tection des patrons de conception avec diffĂ©rentes formes et
différents niveaux de complétude. Les occurrences détectées forment ainsi des opportunités
de refactoring qui seront exploitées pour aboutir à des formes plus souhaitables et/ou plus
complĂštes de ces patrons de conceptions.
Dans le cas dâabsence de connaissance a priori, comme les patrons de conception,
nous proposons une approche basée sur la programmation génétique, pour apprendre des
rÚgles de transformations, capables de détecter des opportunités de refactoring et de les
corriger. Comme alternative Ă la connaissance disponible a priori, lâapproche utilise des
exemples de paires dâartefacts dâavant et dâaprĂšs le refactoring, pour ainsi apprendre les
rĂšgles de refactoring. Nous illustrons cette approche sur le refactoring de modĂšles.Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as
first-class concepts from which validation, code, testing, and documentation are derived.
This paradigm involves various artifacts such as models, meta-models, or model
transformation programs. In an industrial context, these artifacts are increasingly complex.
In particular, their maintenance is time and resources consuming. In order to reduce the
complexity of artifacts and the cost of their maintenance, many researchers have been
interested in refactoring these artifacts to improve their quality.
In this thesis, we propose to study refactoring in MDE holistically, by its application
to these different artifacts. First, we use specific design patterns, as an example of prior
knowledge, applied to model transformations to enable refactoring. We first proceed with a
detecting phase of design patterns, with different forms and levels of completeness. The
detected occurrences thus form refactoring opportunities that will be exploited to implement
more desirable and/or more complete forms of these design patterns.
In the absence of prior knowledge, such as design patterns, we propose an approach
based on genetic programming, to learn transformation rules, capable of detecting
refactoring opportunities and correcting them. As an alternative to prior knowledge, our
approach uses examples of pairs of artifacts before and after refactoring, in order to learn
refactoring rules. We illustrate this approach on model refactoring
Mathematics in Software Reliability and Quality Assurance
This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment