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Although reuse is generally considered a good practice within software engineering, several problems dissuade its industrial application and a new viewpoint is needed. This paper presents a new perspective of reuse based on improved retrieval techniques for semantic content (knowledge). This approach, called Universal Knowledge Reuse Methodology (UKRM), drops the investment costs needed in systematic reuse, including the cost of traceability in the process, and reduces the chaos of ad-hoc reuse. UKRM makes reuse independent of the type of content, the context where it will be reused, and even the user that demands it. The paper includes an incremental experiment in order to validate the feasibility of this proposal.

Process standardization allows to optimize ERP systems and is a nec-essary step prior to ERP implementation projects. Traditional approaches to standardizing business processes are based on manually created "de-jure" process models, which are distorted, error-prone, simplistic, and often deviating from process reality. Theoretically embedded in the organizational contingency theory as kernel theory, this paper employs a design science approach to design a process mining-enabled decision support system (DSS) which combines bottom-up process mining models with manually added top-down standardization infor-mation to recommend a suitable standard process specification from a repository. Extended process models of the as-is process are matched against a repository of best-practice standard process model using an attributebased process similarity matching algorithm. Thus, the DSS aims to reduce the overall costs of process standardization, to optimize the degree of fit between the organization and the implemented processes, and to minimize the degree of organizational change re-quired in standardization and ERP implementation projects. This paper imple-ments a working prototype instantiation in the open-source process analytics platform Apromore based on a real-life event log and standardization attributes for the Purchase-to-Pay and Order-to-Cash processes from three SAP R/3 ERP systems at the industry partner.

Journal of grid computing 16(3), 381 - 408 (2018). doi:10.1007/s10723-018-9453-3, This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications., Published by Springer Nature, Dordrecht

The European Grid Infrastructure (EGI) is providing a sustainable pan---European Computing Grid to support e---Science, integrating more than 350 computing resource centers worldwide. One of its main challenges is that the middleware driving this production infrastructure is constantly evolving, as it adapts to the changing needs of the EGI Community. Since the software is provided by external technology providers, it needs to pass through a quality verification process before it is included in the EGI official release called Unified Middleware Distribution (UMD). Therefore, all software components undergo a release process covering the definition of the functionality and quality criteria, the verification of those criteria, and the testing under production environments. This paper details the design, development and implementation of this software validation process. An analysis is performed on several metrics to evaluate the process impact on the stability of the production infrastructure, by capturing malfunctions and other issues at the initial testing phases.

The increasing complexity and autonomy of modern automotive systems, together with the safety-sensitive nature of many vehicle information flows require a careful analysis of the security requirements and adequate mechanisms for ensuring integrity and confidentiality of data. This is especially true for semi-autonomous vehicle systems, in which user intervention is limited or absent, and information must be trusted. This paper provides a proposal for the representation of high-level security properties in the specification of application components according to the AUTOSAR standard AUTomotive Open System ARchitecture. An automatic generation of security components from security-annotated AUTOSAR specifications is also proposed. It provides for the automatic selection of the adequate security mechanisms based on a high-level specification, thus avoiding complex and error-prone manual encodings by the designer. These concepts and tools are applied to a paradigmatic example in order to show their simplicity and efficacy.

Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new opportunities for application developers. This paper investigates how workflow systems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data. This work is partially funded by the CloudSME Cloud-Based Simulation platform for Manufacturing and Engineering Project No. 608886 (FP7-2013-NMPICT-FOF). Financial support from Programa de Personal Investigador en Formacion Predoctoral from Universidad de ´ Cantabria, co-funded by the regional government of Cantabria, has also been utilized.

Models play a crucial role in the development and maintenance of software systems, but are often neglected during the development process due to the considerable manual effort required to produce them. In response to this problem, numerous techniques have been developed that seek to automate the model generation task with the aid of increasingly accurate algorithms from the domain of Machine Learning. From an empirical perspective, these are extremely challenging to compare; there are many factors that are difficult to control (e.g. the richness of the input and the complexity of subject systems), and numerous practical issues that are just as troublesome (e.g. tool availability). This paper describes the StaMinA (State Machine Inference Approaches) competiton, that was designed to address these problems. The competition attracted numerous submissions, many of which were improved or adapted versions of techniques that had not been subjected to extensive empirical evaluations, and had not been evaluated with respect to their ability to infer models of software systems. This paper shows how many of these techniques substantially improve on the state of the art, providing insights into some of the factors that could underpin the success of the best techniques. In a more general sense it demonstrates the potential for competitions to act as a useful basis for empirical software engineering by (a) spurring the development of new techniques and (b) facilitating their comparative evaluation to an extent that would usually be prohibitively challenging without the active participation of the developers.

We investigate quality improvement in QVT operational mappings (QVTo) model transformations, one of the languages defined in the OMG standard on model-to-model transformations. Two research questions are addressed. First, how can we assess quality of QVTo model transformations? Second, how can we develop higher-quality QVTo transformations? To address the first question, we utilize a bottom---up approach, starting with a broad exploratory study including QVTo expert interviews, a review of existing material, and introspection. We then formalize QVTo transformation quality into a QVTo quality model. The quality model is validated through a survey of a broader group of QVTo developers. We find that although many quality properties recognized as important for QVTo do have counterparts in general purpose languages, a number of them are specific to QVTo or model transformation languages. To address the second research question, we leverage the quality model to identify developer support tooling for QVTo. We then implemented and evaluated one of the tools, namely a code test coverage tool. In designing the tool, code coverage criteria for QVTo model transformations are also identified. The primary contributions of this paper are a QVTo quality model relevant to QVTo practitioners and an open-source code coverage tool already usable by QVTo transformation developers. Secondary contributions are a bottom---up approach to building a quality model, a validation approach leveraging developer perceptions to evaluate quality properties, code test coverage criteria for QVTo, and numerous directions for future research and tooling related to QVTo quality.

Visual GUI Testing (VGT) is a tool-driven technique for automated GUI-based testing that uses image recognition to interact with and assert the correctness of the behavior of a system through its GUI as it is shown to the user. The technique's applicability, e.g. defect-finding ability, and feasibility, e.g. time to positive return on investment, have been shown through empirical studies in industrial practice. However, there is a lack of studies that evaluate the usefulness and challenges associated with VGT when used long-term (years) in industrial practice. This paper evaluates how VGT was adopted, applied and why it was abandoned at the music streaming application development company, Spotify, after several years of use. A qualitative study with two workshops and five well chosen employees is performed at the company, supported by a survey, which is analyzed with a grounded theory approach to answer the study's three research questions. The interviews provide insights into the challenges, problems and limitations, but also benefits, that Spotify experienced during the adoption and use of VGT. However, due to the technique's drawbacks, VGT has been abandoned for a new technique/framework, simply called the Test interface. The Test interface is considered more robust and flexible for Spotify's needs but has several drawbacks, including that it does not test the actual GUI as shown to the user like VGT does. From the study's results it is concluded that VGT can be used long-term in industrial practice but it requires organizational change as well as engineering best practices to be beneficial. Through synthesis of the study's results, and results from previous work, a set of guidelines are presented that aim to aid practitioners to adopt and use VGT in industrial practice. However, due to the abandonment of the technique, future research is required to analyze in what types of projects the technique is, and is not, long-term viable. To this end, we also present Spotify's Test interface solution for automated GUI-based testing and conclude that it has its own benefits and drawbacks.

The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.

Monitoring and repair are two sides of the on-the-fly maintenance of IT systems. Monitoring is well supported by automatic tools. In contrast, repairs involve much higher human intervention which negatively affects reliability and efficiency. The paper introduces a method of automating repairs of IT systems which can be integrated with any of the existing monitoring mechanisms. The method is described as a collection of models and algorithms. The monitored system is modelled as a multi-hierarchy of components, and predicates are used to detect problems and to trigger the corresponding repairs. Repair procedures are represented using flow diagrams. Graphical notations are used to specify the architecture of the runnable component of the proposed method and the corresponding repair API. Formal notation (the Z language) is used to provide for precision and unambiguity of the specifications. The formal specification facilitates reuse of the method in different target environments. For validation purposes, the method has been instantiated (using the PERL language) in an industrial helpdesk supporting the operation of several hundred IT systems. The validation data demonstrate the significant positive impact of the proposed method on the efficiency of the performed repairs.

The Academia-Industry partnership has been increasingly encouraged in the software development field. The main focus of the initiatives is driven by the collaborative work where the scientific research work meets the real needs of the Industry. Aiming to contribute to this effort we have proposed an approach called SoftCoDeR (Software Cooperative Design Research) that combines CMD (Cooperative Method Development), a method of Action Research, to concepts of DSR (Design Science Research). We have applied the SoftCoDeR approach in a software development company to support a research and development project aiming to integrate User Experience practices into the agile software development process. In this paper, we present new findings about this approach that has been extended with the use of Experimental Software Engineering (ESE) practices to conduct experimental validations of artifacts before putting them in industrial practice.

As cloud application platforms (CAPs) are reaching the stage where the human effort required to maintain them at an operational level is unsupportable, one of the major challenges faced by the cloud providers is to develop appropriate mechanisms for run-time monitoring and adaptation, to prevent cloud application platforms from quickly dissolving into a non-reliable environment. In this context, the application of intelligent approaches to Autonomic Clouds may offer promising opportunities. In this paper we present an approach to providing cloud platforms with autonomic capabilities, utilising techniques from the Semantic Web and Stream Reasoning research fields. The main idea of this approach is to encode values, monitored within cloud application platforms, using Semantic Web languages, which then allows us to integrate semantically-enriched observation streams with static ontological knowledge and apply intelligent reasoning. Using such run-time reasoning capabilities, we have developed a conceptual architecture for an autonomous framework and describe a prototype solution we have constructed which implements this architecture. Our prototype is able to perform analysis and failure diagnosis, and suggest further adaptation actions. We report our experience in utilising the Stream Reasoning technique in this context as well as further challenges that arise out of our work.

This paper presents an empirical study based on a set of measures to evaluate the usability of mobile applications running on different mobile operating systems, including Android, iOS and Symbian. The aim is to evaluate empirically a framework that we have developed on the use of the Software Quality Standard ISO 9126 in mobile environments, especially the usability characteristic. To do that, 32 users had participated in the experiment and we have used ISO 25062 and ISO 9241 standards for objective measures by working with two widely used mobile applications: Google Apps and Google Maps. The QUIS 7.0 questionnaire have been used to collect measures assessing the users’ level of satisfaction when using these two mobile applications. By analyzing the results we highlighted a set of mobile usability issues that are related to the hardware as well as to the software and that need to be taken into account by designers and developers in order to improve the usability of mobile applications.

Continued progress in geoinformatics research and applications depends on educating new generations of theorists and practitioners. Effective education and training in turn depend on the availability of appropriate tools, including software. The OpenDragon Project was established in 2004 to help satisfy this requirement. OpenDragon provides a simple yet powerful platform for hands-on lab exercises and student projects. The fact that it is available in multiple languages and can be internationalized without changing any code gives the system a world-wide audience. It is simple, portable, easy to use, and modest in its hardware requirements. OpenDragon can be used as a turnkey platform to teach undergraduates and even secondary school students about geography, agriculture, geology, meteorology, forestry and other natural resource disciplines. OpenDragon is unusual in that it also provides support, through the Programmer’s Toolkit, for students and researchers who need to program their own algorithms or who want to understand the internal workings of remote sensing and GIS software. This paper describes OpenDragon, its history, and its capabilities. It also explains the system architecture and the organization of the Programmer’s Toolkit, and includes some examples of how we have used these capabilities to support student research and to teach geoinformatics programming.

This paper provides an overview of the state of the art technologies for software development in cloud environments. The surveyed systems cover the whole spectrum of cloud-based development including integrated programming environments, code repositories, software modeling, composition and documentation tools, and application management and orchestration. In this work we evaluate the existing cloud development ecosystem based on a wide number of characteristics like applicability (e.g. programming and database technologies supported), productivity enhancement (e.g. editor capabilities, debugging tools), support for collaboration (e.g. repository functionality, version control) and post-development application hosting and we compare the surveyed systems. The conducted survey proves that software engineering in the cloud era has made its initial steps showing potential to provide concrete implementation and execution environments for cloud-based applications. However, a number of important challenges need to be addressed for this approach to be viable. These challenges are discussed in the article, while a conclusion is drawn that although several steps have been made, a compact and reliable solution does not yet exist.

The key objective of No-Reference (NR) visual metrics (indicators) is to predict the end-user experience concerning remotely delivered video content. Rapidly increasing demand for easily accessible, high quality video material makes it crucial for service providers to test the user experience without the need for comparison with reference material. In this paper, we present a versatile measurement system and describe various optimisation strategies utilised to reach real-time operation. Furthermore, several calculation automation scripts are described, along with a dedicated graphical user interface, which gives a more comprehensive insight into the presented system. On top of that, we show the results of crowd-sourcing experiments used to estimate subjective threshold values for quality indicators. Additionally, integration with the IMCOP system is introduced.

Software libraries and frameworks play an important role in software system development. The appropriate usage of their functionalities/components through their APIs, however, is a challenge for developers. Usually, API documentation, when it exists, is insufficient to assist them in their programming tasks. There are few API documentation writers for the many potential readers, resulting in the lack of explanations and examples concerning different scenarios and perspectives. The interaction of developers on the Web, on the other hand, generates content concerning APIs from different perspectives, which can be used to document APIs, also known as crowd documentation. In this paper, we present a study regarding the knowledge generated by the crowd on the Stack Overflow question-and-answer website. Our main goal is to understand how the crowd can contribute for API documentation on two programming tasks: how to implement a scenario using an API (how-to-do-it), and how to fix domain-independent bugs in an existing code where there was a misunderstanding regarding the usage of an API (debug-corrective). We classified questions available on Stack Overflow by the main concerns of askers, and we used those classified as how-to-do-it and debug-corrective to analyze the coverage of API elements on the discussions related to such questions. Our cases included the well-known and popular Swing and Android APIs. Our main findings showed that the crowd provides more content for debug-corrective tasks than for how-to-do-it tasks, regardless of the API. Android API elements are more discussed by the crowd compared to Swing. Moreover, we observed that some API elements are frequently mentioned together in discussions, and that there is a strong association between API coverage on Stack Overflow and its usage in real software systems. Crowd documentation may not be a complete substitute for official documentation because of its partial coverage, especially for how-to-do-it tasks. However, it can still significantly enhance the existent documentation, especially for the most commonly used API elements, providing code samples and explanations on a large variety of usage nuances. Finally, taking advantage of the high coverage for debug-corrective tasks, a new kind of debugging assistant may be conceived.

Cloud computing offers various computational resources via convenient on-demand service provision. Currently, heterogeneous services and cloud resources are usually utilized and managed through diverse service portals. This significantly limits the effectiveness and efficiency for tasks implementation. Fundamentally, it is due to the lack of adequate specifications for service concepts, operations and interfaces from diverse cloud service models and types. This paper proposes a service management operation semantic description framework for comprehensive cloud service operation specification. Relying on ontological modelling techniques, cloud service operations are specified via entity classification, attribute assertion, relationship assertion and annotation assertion. Further, the proposed framework benefits from operation reasoning application. It enables intelligent assistances for multiple operation preparation and remote execution tasks. Based on the approach, a cloud service operation ontology and a unified service access and manipulation system prototype are implemented. Extensive experiments are conducted over different cloud service providers and for distinct service models. Obtained results demonstrate that the approach outperforms existing practices by facilitating reliable and effective service access, manipulation and interaction tasks.

Among research opportunities in software engineering for cloud computing model, interoperability stands out. We found that the dynamic nature of cloud technologies and the battle for market domination make cloud applications locked-id, i.e, proprietary, non-portable and non-interoperable. In general context of cloud computing, interoperability goes beyond communication between systems like in other fields, it goes in direction of more dynamic, heterogeneous, complex and composed applications that take advantage of best features from different providers and services simultaneously. Interoperability in cloud constitutes a great challenge that must be overcome for that, in the future, software be more dynamic and improved. Objective: This paper aims at identifying how interoperability in cloud computing has been addressed in the existing literature, offering an up-to-date view of concepts relate to how to develop interoperable software that takes advantage of different cloud models. Thus, providing a basis for further research in the field and consolidating e better exploring existing concepts. Method: To fulfill this objective, we surveyed literature. We defined six research questions and conducted the study according to a protocol that included planning, and execution. Results: A first result of the review is that there is no well established definition for cloud interoperability. This study also identified cloud interoperability concepts (e.g., cloud brokers, multi-cloud and cloud federation), requirements for interoperable applications and existing cloud interoperability solutions, showing that these are either too specific for particular situations. Finally, the survey found no evaluation models for cloud interoperability solutions. We also present a discussion on the findings of this study. Conclusion: Since the study observed that there are no well-established cloud interoperability solutions yet, we conclude that the issues raised by lack of interoperability persist. Selecting one interoperable solution or even a cloud standard can free the system from the underlying providers, but it would still be locked into the selected particular solution.

In this paper we address the problem of testing complex computer models for infrastructure protection and emergency response based on detailed and realistic application scenarios using advanced computational methods and tools. Specifically, we focus here on testing situation analysis decision support models for marine safety & security operations as a sample application domain. Arguably, methodical approaches for analyzing and validating situation analysis methods, decision support models, and information fusion algorithms require realistic vignettes that describe in great detail how a situation unfolds over time depending on initial configurations, dynamic environmental conditions and uncertain operational aspects. Meaningful results from simulation runs require appropriate test cases, the production of which is in itself a complex activity. To simplify this task, we introduce here the conceptual design of a Vignette Generator that has been developed and tested in an industrial research project. We also propose a framework for composing vignettes from reusable vignette elements together with a formal representation for vignettes using the Abstract State Machine method and illustrate the approach by means of various practical examples.