15 research outputs found

    A framework for software reference architecture analysis and review

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    Premio al mejor artículo, X Workshop Latinoamericano Ingeniería de Software Experimental, ESELAW 2013Tight time-to-market needs pushes software companies and IT consulting firms to continuously look for techniques to improve their IT services in general, and the design of software architectures in particular. The use of soft-ware reference architectures allows IT consulting firms reusing architectural knowledge and components in a systematic way. In return, IT consulting firms face the need to analyze the return on investment in software reference architectures for organizations, and to review these reference architectures in order to ensure their quality and incremental improvement. Little support exists to help IT consulting firms to face these challenges. In this paper we present an empirical framework aimed to support the analysis and review of software reference architectures and their use in IT projects by harvesting relevant evidence from the wide spectrum of involved stakeholders.Award-winningPostprint (author’s final draft

    Framework for evaluating and selecting mobile-learning applications using multi criteria decision making techniques

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    The use of mobile learning (m-learning) applications in education has increased dramatically in recent years. M-learning applications are installed by users through a variety of mobile device distribution platforms. For a wide audience to accept them, these applications must be stable and of high quality. The decision to purchase m-learning applications needs systematic guidelines so that the appropriate one can be selected to provide a viable and effective solution to educational organizations. Usability in m-learning applications has been studied as a non-functional problem in several previous studies. In reality, Saudi tertiary institutions still lack a systematic, efficient, and well-defined framework for evaluating and selecting m-learning applications due to the lack of reliable m-learning application selection methods. Therefore, this study addresses this gap by proposing a framework to support and improve m-learning applications evaluation and selection process named as Mobile-Learning Application Evaluation and Selection Framework (MLA-ESF). MLA-ESF supports evaluation and selection of m-learning applications and integration of functional and non-functional requirements as well as addresses mismatch problems. In addition, the MLA-ESF is developed to assist and guide developers and educational organizations in selecting the required m-learning application in a more systematic and repeatable manner. Moreover, the MLA-ESF framework provides a guideline for future theoretical research, as well as being a practical and usable tool in real contexts. The study is conducted in four main phases: a survey and interview of decision-makers and users to identify the evaluation criteria, development of the framework based on the Evaluation Theory, development of a new decision-making technique by integrating Fuzzy Analytical Hierarchy Process (FAHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and GAP Analysis (GA) to handle user requirements mismatches, and validation of the applicability and reliability of MLAESF using experts review, case study and yardstick validation. The study shows that the evaluated aspects of MLA-ESF namely, inputs, actions, outcomes, are feasible and demonstrate their potential and applicability to be applied in the real environment as 75% of the experts found it as useful, 66.7% find it easy to implement, and 75% find the techniques are adequate and sufficient

    Benefits and drawbacks of software reference architectures: A case study

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    Context: Software Reference Architectures (SRAs) play a fundamental role for organizations whose business greatly depends on the efficient development and maintenance of complex software applications. However, little is known about the real value and risks associated with SRAs in industrial practice. Objective: To investigate the current industrial practice of SRAs in a single company from the perspective of different stakeholders. Method An exploratory case study that investigates the benefits and drawbacks perceived by relevant stakeholders in nine SRAs designed by a multinational software consulting company. Results: The study shows the perceptions of different stakeholders regarding the benefits and drawbacks of SRAs (e.g., both SRA designers and users agree that they benefit from reduced development costs; on the contrary, only application builders strongly highlighted the extra learning curve as a drawback associated with mastering SRAs). Furthermore, some of the SRA benefits and drawbacks commonly highlighted in the literature were remarkably not mentioned as a benefit of SRAs (e.g., the use of best practices). Likewise, other aspects arose that are not usually discussed in the literature, such as higher time-to-market for applications when their dependencies on the SRA are managed inappropriately. Conclusions: This study aims to help practitioners and researchers to better understand real SRAs projects and the contexts where these benefits and drawbacks appeared, as well as some SRA improvement strategies. This would contribute to strengthening the evidence regarding SRAs and support practitioners in making better informed decisions about the expected SRA benefits and drawbacks. Furthermore, we make available the instruments used in this study and the anonymized data gathered to motivate others to provide similar evidence to help mature SRA research and practice.Peer ReviewedPostprint (author's final draft

    Analysis of Artificial Intelligence based diagnostic methods for satellites

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    The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed

    A framework to improve the architecture quality of software intensive systems

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    Over the past decade, the amount and complexity of software for almost any business sector has increased substantially. Unfortunately, the increased complexity of software in the systems to be built has often lead to a significant mismatch between the planned and the implemented products. One common problem is that system-wide quality attributes such as safety, reliability, performance, and modifiability are not sufficiently considered in software architecture design. Typically, they are addressed in an ad-hoc and unstructured fashion. Since rationales for architectural decisions are frequently missing, risks associated with those decisions can be neither identified, nor mitigated in a systematic way. Consequently, there is a high probability that the resulting software architecture fails to meet business goals and does not allow the building of an adequate system. This work presents QUADRAD, a framework for Quality-Driven Architecture Development. QUADRAD is capable of improving architecture quality for software-intensive systems in a systematic way. It supports the development of architectures that are optimized according to their essential quality requirements. Such architectures permit the building of systems that are better aligned to the principal market needs and business goals. QUADRAD is complemented by the Architecture Exploration Tool (AET), which supports architecture evaluations and helps in documenting the fundamental design decisions of an architecture. QUADRAD has been validated in three industrial projects. For each of these projects the architecture quality could be significantly increased. The results confirm the hypothesis of this work and demonstrate how critical problems in the transition from requirements to architecture design can be mitigated
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