1,014,856 research outputs found

    A Framework for Evaluating Model-Driven Self-adaptive Software Systems

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    In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

    Workshop proceedings of the 1st workshop on quality in modeling

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    Quality assessment and assurance constitute an important part of software engineering. The issues of software quality management are widely researched and approached from multiple perspectives and viewpoints. The introduction of a new paradigm in software development – namely Model Driven Development (MDD) and its variations (e.g., MDA [Model Driven Architecture], MDE [Model Driven Engineering], MBD [Model Based Development], MIC [Model Integrated Computing]) – raises new challenges in software quality management, and as such should be given a special attention. In particular, the issues of early quality assessment, based on models at a high abstraction level, and building (or customizing the existing) prediction models for software quality based on model metrics are of central importance for the software engineering community. The workshop is continuation of a series of workshops on consistency that have taken place during the subsequent annual UML conferences and recently MDA-FA. The idea behind this workshop is to extend the scope of interests and address a wide spectrum of problems related to MDD. It is also in line with the overall initiative of the shift from UML to MoDELS. The goal of this workshop is to gather researchers and practitioners interested in the emerging issues of quality in the context of MDD. The workshop is intended to provide a premier forum for discussions related to software quality and MDD. And the aims of the workshop are: - Presenting ongoing research related to quality in modeling in the context of MDD, - Defining and organizing issues related to quality in the MDD. The format of the workshop consists of two parts: presentation and discussion. The presentation part is aimed at reporting research results related to quality aspects in modeling. Seven papers were selected for the presentation out of 16 submissions; the selected papers are included in these proceedings. The discussion part is intended to be a forum for exchange of ideas related to understanding of quality and approaching it in a systematic way

    Workshop proceedings of the 1st workshop on quality in modeling

    Get PDF
    Quality assessment and assurance constitute an important part of software engineering. The issues of software quality management are widely researched and approached from multiple perspectives and viewpoints. The introduction of a new paradigm in software development – namely Model Driven Development (MDD) and its variations (e.g., MDA [Model Driven Architecture], MDE [Model Driven Engineering], MBD [Model Based Development], MIC [Model Integrated Computing]) – raises new challenges in software quality management, and as such should be given a special attention. In particular, the issues of early quality assessment, based on models at a high abstraction level, and building (or customizing the existing) prediction models for software quality based on model metrics are of central importance for the software engineering community. The workshop is continuation of a series of workshops on consistency that have taken place during the subsequent annual UML conferences and recently MDA-FA. The idea behind this workshop is to extend the scope of interests and address a wide spectrum of problems related to MDD. It is also in line with the overall initiative of the shift from UML to MoDELS. The goal of this workshop is to gather researchers and practitioners interested in the emerging issues of quality in the context of MDD. The workshop is intended to provide a premier forum for discussions related to software quality and MDD. And the aims of the workshop are: - Presenting ongoing research related to quality in modeling in the context of MDD, - Defining and organizing issues related to quality in the MDD. The format of the workshop consists of two parts: presentation and discussion. The presentation part is aimed at reporting research results related to quality aspects in modeling. Seven papers were selected for the presentation out of 16 submissions; the selected papers are included in these proceedings. The discussion part is intended to be a forum for exchange of ideas related to understanding of quality and approaching it in a systematic way

    A Framework for Model-Driven Development of Mobile Applications with Context Support

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    Model-driven development (MDD) of software systems has been a serious trend in different application domains over the last 15 years. While technologies, platforms, and architectural paradigms have changed several times since model-driven development processes were first introduced, their applicability and usefulness are discussed every time a new technological trend appears. Looking at the rapid market penetration of smartphones, software engineers are curious about how model-driven development technologies can deal with this novel and emergent domain of software engineering (SE). Indeed, software engineering of mobile applications provides many challenges that model-driven development can address. Model-driven development uses a platform independent model as a crucial artifact. Such a model usually follows a domain-specific modeling language and separates the business concerns from the technical concerns. These platform-independent models can be reused for generating native program code for several mobile software platforms. However, a major drawback of model-driven development is that infrastructure developers must provide a fairly sophisticated model-driven development infrastructure before mobile application developers can create mobile applications in a model-driven way. Hence, the first part of this thesis deals with designing a model-driven development infrastructure for mobile applications. We will follow a rigorous design process comprising a domain analysis, the design of a domain-specific modeling language, and the development of the corresponding model editors. To ensure that the code generators produce high-quality application code and the resulting mobile applications follow a proper architectural design, we will analyze several representative reference applications beforehand. Thus, the reader will get an insight into both the features of mobile applications and the steps that are required to design and implement a model-driven development infrastructure. As a result of the domain analysis and the analysis of the reference applications, we identified context-awareness as a further important feature of mobile applications. Current software engineering tools do not sufficiently support designing and implementing of context-aware mobile applications. Although these tools (e.g., middleware approaches) support the definition and the collection of contextual information, the adaptation of the mobile application must often be implemented by hand at a low abstraction level by the mobile application developers. Thus, the second part of this thesis demonstrates how context-aware mobile applications can be designed more easily by using a model-driven development approach. Techniques such as model transformation and model interpretation are used to adapt mobile applications to different contexts at design time or runtime. Moreover, model analysis and model-based simulation help mobile application developers to evaluate a designed mobile application (i.e., app model) prior to its generation and deployment with respected to certain contexts. We demonstrate the usefulness and applicability of the model-driven development infrastructure we developed by seven case examples. These showcases demonstrate the designing of mobile applications in different domains. We demonstrate the scalability of our model-driven development infrastructure with several performance tests, focusing on the generation time of mobile applications, as well as their runtime performance. Moreover, the usability was successfully evaluated during several hands-on training sessions by real mobile application developers with different skill levels

    Scalable aggregation predictive analytics: a query-driven machine learning approach

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    We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

    A Quality Model in a Quality Evaluation Framework for MDWE methodologies

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    Nowadays, diverse development methodologies exist in the field of Model-Driven Web Engineering (MDWE), each of which covers different levels of abstraction on Model-Driven Architecture (MDA): CIM, PIM, PSM and Code. Given the high number of methodologies available, it is necessary to evaluate the quality of existing methodologies and provide helpful information to the developers. Furthermore, proposals are constantly appearing and the need may arise not only to evaluate the quality but also to find out how it can be improved. In this context, QuEF (Quality Evaluation Framework) can be employed to assess the quality of MDWE methodologies. This article presents the work being carried out and describes tasks to define a Quality Model component for QuEF. This component would be responsible for providing the basis for specifying quality requirements with the purpose of evaluating quality.Ministerio de Educación y Ciencia TIN2007-67843-C06-03Ministerio de Educación y Ciencia TIN2007-30391-

    A Framework for Evaluating Model-Driven Self-adaptive Software Systems

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    In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers

    Detecting the Onset of Dementia using Context-Oriented Architecture

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    In the last few years, Aspect Oriented Software De- velopment (AOSD) and Context Oriented Software Development (COSD) have become interesting alternatives for the design and construction of self-adaptive software systems. An analysis of these technologies shows them all to employ the principle of the separation of concerns, Model Driven Architecture (MDA) and Component-based Software Development (CBSD) for building high quality of software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the adaptability, and dependability of software systems. COSD, has emerged as a generic devel- opment paradigm towards constructing self-adaptive software by integrating MDA with context-oriented component model. The self-adaptive applications are developed using a Context- Oriented Component-based Applications Model-Driven Architec- ture (COCA-MDA), which generates an Architecture Description language (ADL) presenting the architecture as a components- based software system. COCA-MDA enables the developers to modularise the application based on their context-dependent behaviours, and separate the context-dependent functionality from the context-free functionality of the application. In this article, we wish to study the impact of the decomposition mechanism performed in MDA approaches over the software self-adaptability. We argue that a better and significant advance in software modularity based on context information can increase software adaptability and increase their performance and modi- fiability

    Modelling mobile health systems: an application of augmented MDA for the extended healthcare enterprise

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    Mobile health systems can extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a model-driven design and development methodology for the development of the m-health components in such extended enterprise computing systems. The methodology applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. Recent work on modelling applications from the healthcare domain is reported. One objective of this work is to explore and elaborate the proposed methodology. At the University of Twente we are developing m-health systems based on Body Area Networks (BANs). One specialization of the generic BAN is the health BAN, which incorporates a set of devices and associated software components to provide some set of health-related services. A patient will have a personalized instance of the health BAN customized to their current set of needs. A health professional interacts with their\ud patients¿ BANs via a BAN Professional System. The set of deployed BANs are supported by a server. We refer to this distributed system as the BAN System. The BAN system extends the enterprise computing system of the healthcare provider. Development of such systems requires a sound software engineering approach and this is what we explore with the new methodology. The methodology is illustrated with reference to recent modelling activities targeted at real implementations. In the context of the Awareness project BAN implementations will be trialled in a number of clinical settings including epilepsy management and management of chronic pain

    NDT-Suite: A Methodological Tool Solution in the Model-Driven Engineering Paradigm

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    Although the Model-Driven paradigm is being accepted in the research environment as a very useful and powerful option for effective software development, its real application in the enter prise context is still a challenge for software engineering. Several causes can be stacked out, but one of them can be the lack of tool support for the efficient application of this paradigm. This pa per presents a set of tools, grouped in a suite named NDT-Suite, which under the Model-Driven paradigm offer a suitable solution for software development. These tools explore different options that this paradigm can improve such as, development, quality assurance or requirement treat ment. Besides, this paper analyses how they are being successfully applied in the industryMinisterio de Ciencia e Innovación TIN2013-46928-C3-3-RJunta de Andalucía TIC-578
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