Abstract With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have imposed de facto standards for quality assessment in fast‐paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality‐in‐use metrics in order to define an absolute scale of comparison for web component quality.

Business process analysis is a key factor in the lifecycle of Robotic Process Automation. Currently, task mining techniques provide mechanisms to analyze information about the process tasks to be automated, e.g., identify repetitive tasks or process variations. Existing proposals mainly rely on the user interactions with the UIs of the system (i.e., keyboard and mouse level) and information that can be gathered from them (e.g., the window name) which is stored in a UI event log. In some contexts, the latter information is limited because the system is accessed through virtualized environments (e.g., Citrix or Teamviewer). Other approaches extend the UI Log, including screenshots to address this issue. Regardless of the context, the aim is to store as much information as possible in the UI Log so that is can be analyzed later on, e.g., by extracting features from the screenshots. This amount of information can introduce much noise in the log that messes up what is relevant to the process. To amend this, the current approach proposes a method to include a gaze analyzer, which helps to identify which is process-relevant information between all the information. More precisely, the proposal extends the UI Log definition with the attention change level, which records when the user’s attention changes from one element on the screen to another. This paper sets the research settings for the approach and enumerates the future steps to conduct it.

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low-level metrics, the strategies defined to ensure compliance with the established service-level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock-in and prevents consumers from benefiting from the advantages of multi-cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge-based system capable of automatically translating high-level SLAs defined by cloud consumers into a set of conditions expressed as vendor-neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor-neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi-cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi-cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.

Event-driven architectures are becoming more prevalent recently in multiple technological paradigms, especially in web applications, with message brokers being the cornerstone of these architectures. One of the most relevant implementations of these message brokers are content-based publish/subscribe systems. The performance of these systems is a critical factor for web engineering, since the web applications they support need to be reactive despite increases and fluctuations in workloads. However, an obstacle to the research of these systems is the lack of real and publicly available workloads, due to the privacy issue involved in disclosing the interests (subscriptions) of users and other commercial interests of the companies. In this paper we present a parameterizable automated system designed to syntactically translate workloads from different content-based publish/subscribe systems as a means to increase the availability of public workloads to solve the aforementioned problem. As a case study, we describe the evolution of a context-aware content-based publish/subscribe system (i.e. E-SilboPS) designed by the authors, which improves up to 5 times the performance of its previous version by reaching the maximum throughput limited by the physical resources of the hardware where it is deployed, as demonstrated by the conducted quantitative evaluation. Then, we validate the utility of the proposed automated workload generation system by using it to make the performance comparison between this new version E-SilboPS and one of the most cited publish/subscribe systems called PADRES, through a real trace of a massively multiplayer online game (MMOG) generated by the latter.

Abstract As the number of cyber‐attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber‐security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network‐based cyber‐attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber‐attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber‐crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.

Context: Measuring the Software Development Process (SDP) supports organizations in their endeavor to understand, manage, and improve their development processes and projects. In the last decades, the SDP has evolved to meet the market needs and keep abreast of modern technologies and infrastructures. These changes in the development processes have increased the importance of the measurement and caused changes in the measurement process and the used measures. Objective: This work aims to develop a solution to support the measurement activities throughout the process lifecycle. Method: Study the current state of the art to identify existing gaps. Then, propose a solution to support the process measurement throughout the SDP lifecycle. Results: The proposed solution consists of two main components: (i) Measurement lifecycle, which defines the measurement activities throughout the SDP lifecycle, (ii) Measurement definition metamodel (MDMM), which supports the measurement lifecycle and its integration into the process lifecycle. Conclusion: This proposal allows organizations to define, manage, and improve their processes; the proposed information model supports the unification of the measurement concepts and vocabulary. The defined measurement lifecycle provides a comprehensive guide for the organizations to establish the measurement objectives and carry out the necessary activities to achieve them. The proposed MDMM supports and guides the engineers in the complete and operational definition of the measurement concepts.

Blockchain technology is a chain of cryptographically linked blocks. It was designed to be immutable, so that the identity and traceability of the information entered would be guaranteed. After analyzing several traceability solutions, in the context of a Spanish company project, it was found that in order for a traceability solution to be efficient and agile, an additional layer is necessary in the blockchain. Since this need originated in the industrial sector, the subject has awakened considerable interest in the research community. This paper explains why the extra layer is essential and why it should ideally be totally independent of the information that is recorded on the blockchain network. Although data in a blockchain network is immutable, the paper also outlines the need for additional verification mechanisms capable of determining whether the raw data was correct. Finally, it includes planned future work.

Abstract Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering‐based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance‐related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.

Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct.

Abstract Acquiring the skills needed to solve certain types of problems is one of the main challenges of distance university education, demanding extra motivation from students to tackle it. New technology should be one of our great allies in addressing these problems. This article proposes an expert system with a web‐based problem‐solving simulator for a multidevice environment in order to allow students to access an unlimited number of problems with their corresponding solutions, immediately, anytime and anywhere. The types of problems that can be used are those based on quantitative methods with a fixed methodology to be followed. A successful case study was carried out for the subject Operations Management at the Distance University of Madrid (UDIMA), with three different problem simulators having been developed. The results were very satisfactory compared with previous academic years in terms of motivation. A decrease in students that did not sit the final examination was observed, as was an increase in the number of visits to the Moodle classroom. Students also responded positively in their assessments of the initiative.

GUI is one of the key aspect of an information system from the point of view of customers and users.This paper introduces PLEC, a participative process for designing GUI interfaces with the collaboration of the final users and stakeholders.Participants do not need technical knowledge of GUI prototype.A case study has been developed and carried out to verify if PLEC process is feasible.

Over last decade, Assisted Reproductive Treatment (ART) has become a very used health service by more and more people around the world because of problems such as the delay in the maternity age, singleparent couples, etc.In this context, health agencies have performed innovations to improve healthcare processes of ARTs, to optimize the performance of health professionals who work in fertilization laboratories and to improve Biological Sample Management (BSM) and sample traceability in ART.However, there are important handicaps in ART processes from the point of view of quality, safety and management.On the one hand, these processes are mainly based on manual execution tasks and manual control tasks.This excess of manual tasks could lead to

Many of the problems arising from rapid urbanization and urban population growth can be solved by making cities “smart”. These smart cities are supported by large networks of interconnected and widely geo‐distributed devices, known as Internet of Things or IoT, that generate large volumes of data. Traditionally, cloud computing has been the technology used to support this infrastructure; however, some of the essential requirements of smart cities such as low‐latency, mobility support, location‐awareness, bandwidth cost savings, and geo‐distributed nature of such IoT systems cannot be met. To solve these problems, the fog computing paradigm proposes extending cloud computing models to the edge of the network. However, most of the proposed architectures and frameworks are based on their own private data models and interfaces, which severely reduce the openness and interoperability of these solutions. To address this problem, we propose a standard‐based fog computing architecture to enable it to be an open and interoperable solution. The proposed architecture moves the stream processing tasks to the edge of the network through the use of lightweight context brokers and Complex Event Processing (CEP) to reduce latency. Moreover, to communicate the different smart cities domains we propose a Context Broker based on a publish/subscribe middleware specially designed to be elastic and low‐latency and exploit the context information of these environments. Additionally, we validate our architecture through a real smart city use case, showing how the proposed architecture can successfully meet the smart cities requirements by taking advantage of the fog computing approach. Finally, we also analyze the performance of the proposed Context Broker based on microbenchmarking results for latency, throughput, and scalability.

Modern IoT deployments do require considerable investments that might only be justified if the data being gathered could be monetized, which leads to the need for a digital data marketplace. In many cases, the provider of the IoT data needs to process it locally for data curation, aggregation, stream processing, etc. At the same time, the consumer could be interested in nearby data. This scenario resembles a fog computing architecture where companies require being able, keeping data under their control, to securely make it available to other companies in a peer‐to‐peer fashion, without needing a cloud intermediary (like traditional marketplaces do), thus maximizing the locality of the processing and avoiding the existence of a bottleneck when the intermediary makes the data delivery for accounting purposes. Nevertheless, this imposes a hard requirement: by not having a central marketplace, the peers (seller and customer) need to trust each other, which, in turn, requires enforcing a nonrepudiation schema. In this paper, the authors propose a distributed peer‐to‐peer architecture for such a data marketplace that takes advantage of the architectural fundamentals of fog computing, in which data processing, filtering, and stream based event generation is done in a fog node along with the data, and where relationships, both commercial agreements and data delivery, are performed directly between producers and consumers without the need of mutual trust thanks to the usage of blockchain principles (e.g., distributed ledger, consensus mechanism). The proposed architecture is validated through a case study involving a set of key issues regarding nonrepudiation commonly identified when moving from a centralized marketplace to a distributed one. Moreover, it is shown that the proposed solution does not bring in any limitation with regard to a centralized marketplace solution, in terms of pricing models (subscriptions, pay‐per‐use, etc.) or usage conditions (contract duration, updates rate, etc.).

Web components improve Internet applications, empowering end-user development with encapsulation and interoperability. Public repositories contain a variety of web components implementing GUI elements, system components for mashup development and wrappers of popular web services. The distribution of this technology through public repositories has fostered a growing interest in quality metrics for web components, highlighting the absence of unified approach. This paper presents a reference model and evaluation framework for measuring the quality of web components. Preliminary results of an experimentation framework provide insights for comparing standard metrics with custom metrics based on curated user-perceived quality and data extracted from public repositories.

This article addresses one of the major end-user software engineering (EUSE) challenges, namely, how to motivate end users to apply unfamiliar software engineering techniques and activities to achieve their goal: translate requirements into software that meets their needs. EUSE activities are secondary to the goal that the program is helping to achieve and end-user programming is opportunistic. The challenge is then to find ways to incorporate EUSE activities into the existing workflow without users having to make substantial changes to the type of work they do or their priorities. In this article, we set out an approach to EUSE for web-based applications. We also propose a software lifecycle that is consistent with the conditions and priorities of end users without programming skills and is well-aligned with EUSE's characteristic informality, ambiguity and opportunisticness. Users applying this lifecycle manage to find solutions that they would otherwise be unable to identify. They also develop quality products. Users of this approach will not have to be acquainted with software engineering, as a framework will take them through the web-centred EUSE lifecycle step-by-step. We also report a statistical experiment in which users develop web software with and without a framework to guide them through the lifecycle. Its aim is to validate the applicability of our framework-driven lifecycle.

Although context could be exploited to improve the performance, elasticity and adaptation in most distributed systems that adopt the publish/subscribe (P/S) model of communication, only very few works have explored domains with highly dynamic context, whereas most adopted models are context agnostic. In this paper, we present the key design principles underlying a novel context-aware content-based P/S (CA-CBPS) model of communication, where the context is explicitly managed, focusing on the minimization of network overhead in domains with recurrent context changes thanks to contextual scoping. We highlight how we dealt with the main shortcomings of most of the current approaches. Our research is some of the first to study the problem of explicitly introducing context-awareness into the P/S model to capitalize on contextual information. The envisioned CA-CBPS middleware enables the cloud ecosystem of services to communicate very efficiently, in a decoupled, but contextually scoped fashion.

Over the past few years, traditional software products, sales and licensing schemes have declined, whereas business value and revenues have shifted to SaaS-based schemes. Even so, most research has focused primarily on the technical layer (i.e. service invocation, integration, coordination, etc.). As a result, most SOA solutions available on market still do not feature a service "face" for human users. The SOA front-end of those that do is typically based on monolithic, rigid and non-customizable user interfaces and portals that invoke back-end services and processes ad-hoc. This paper presents the rationale behind a novel user-centered visual service composition system being developed by the European FP7 FAST Project consortium. This service composition system aims to enable service composition by guiding non-technical users through an open innovation process. The proposal formally models the component model, techniques and languages. Also it leverages some well-known Web 2.0 principles in order to bridge the gap between the service technical layer of a SOA and its end users. This should improve user appreciation of the benefits of such a system, enabling them to easily mash up their own service front-end from its basic and/or available building blocks.

Service oriented architectures (SOAs) based on Web Services have attracted a great interest and IT investments during the last years, principally in the context of business-to-business integration within corporate intranets. However, they are nowadays evolving to break through enterprise boundaries, in a revolutionary attempt to make the approach pervasive, leading to what we call a user-centric SOA, i.e. a SOA conceived as a Web of Services made up of compositional resources that empowers end-users to ubiquitously exploit these resources by collaboratively remixing them. In this paper we explore the architectural basis, technologies, frameworks and tools considered necessary to face this novel vision of SOA. We also present the rationale behind EzWeb/FAST: an undergoing EU funded project whose first outcomes could serve as a preliminary proof of concept.