Responding to Enterprise Architecture Initiatives: Loyalty, Voice and Exit

Many large organizations have on-going Enterprise Architecture initiatives. Key aims include achieving more organizational agility, and to tidy up a messy portfolio of IT silo systems. A holistic approach to IT architecture has been an accepted strategy, but the results of these initiatives have been variable. An under-researched aspect is how different organizational units respond to the call for a holistic approach. In this study, we investigate how different stakeholders connected to three ongoing projects responded to the call for EA. With a qualitative approach, we identify three options of response to EA initiatives: (i) compliance with the EA strategy, (ii) loyal but isolated response, and (iii) rebel solutions. We argue for the need of a more nuanced repertoire of actions for dealing with EA, and show how these responses are useful for understanding and managing successful EA

C4 model in a Software Engineering subject to ease the comprehension of UML and the software development process

Software engineering provides the competences and skills to design and develop robust, secure and efficient applications that solve real problems. Students have to develop their abstract thinking to find solutions taking into account not only technical development, but economic and social impact. In previous years, different changes have been introduced in the teaching methods with significant outcomes. However, students are still facing difficulties with one of the core contents of the subject, UML. For this reason, the present work aims to introduce C4 model as a complement of the existing UML diagrams. This proposal uses the two first levels of the C4 model to complement the requirements elicitation process, traditionally based only on use cases, to let students start the design of their systems without going into greater technical details

Evaluating pedagogical practices supporting collaborative learning for model-based system development courses

Model-based software development (MBSD) has been widely used in industry for its effectiveness of code generation, code reuse and system evolution. At different stages of the software lifecycle, models -- as opposed to actual code -- are used as abstractions to present software development artifacts. In a university software engineering curriculum, compared to other concrete and tangible courses, e.g., game and app development, these levels of abstraction are often difficult for students to understand, and further, to see models' usefulness in practice. This paper presents an evaluation of pedagogical practices supporting collaborative learning for MBSD courses from experiences of teaching them at University of Oslo. The focus is to answer two research questions: 1) What are the challenges and possibilities when using a collaborative learning approach for teaching modelling and architecture? 2) What are the challenges and benefits of having a holistic approach to MBSD courses in light of the requirements of academia and the needs of industry? The term “holistic” is understood 1) as an approach that involves human factors (users), technology and processes, 2) as an approach to teaching MBSD courses where modelling for Enterprise Architecture is taught together with System Architecture and Model-Driven Language Engineering. Empirical data was collected through interviews, questionnaires, and document analysis. The paper’s research results show that three different course perspectives (Modeling for Enterprise Architecture with Business Architecture, System Architecture and Model Driven Language Engineering) are essential parts of teaching modeling courses, and an industry field study shows that industry sees the potential of having junior architects to provide support to a team and solving basic architectural problems

Tourist experiences recommender system based on emotion recognition with wearable data

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.This research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja. We thank the members of the GICAC group (Research Group in Administrative and Accounting Sciences) of the Universidad Santo Tomás Seccional Tunja for their participation in the experimental phase of this investigation

A mapping study on documentation in Continuous Software Development

Context: With an increase in Agile, Lean, and DevOps software methodologies over the last years (collectively referred to as Continuous Software Development (CSD)), we have observed that documentation is often poor. Objective: This work aims at collecting studies on documentation challenges, documentation practices, and tools that can support documentation in CSD. Method: A systematic mapping study was conducted to identify and analyze research on documentation in CSD, covering publications between 2001 and 2019. Results: A total of 63 studies were selected. We found 40 studies related to documentation practices and challenges, and 23 studies related to tools used in CSD. The challenges include: informal documentation is hard to understand, documentation is considered as waste, productivity is measured by working software only, documentation is out-of-sync with the software and there is a short-term focus. The practices include: non-written and informal communication, the usage of development artifacts for documentation, and the use of architecture frameworks. We also made an inventory of numerous tools that can be used for documentation purposes in CSD. Overall, we recommend the usage of executable documentation, modern tools and technologies to retrieve information and transform it into documentation, and the practice of minimal documentation upfront combined with detailed design for knowledge transfer afterwards. Conclusion: It is of paramount importance to increase the quantity and quality of documentation in CSD. While this remains challenging, practitioners will benefit from applying the identified practices and tools in order to mitigate the stated challenges

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.This work was supported by the Spanish Ministry of Science and Innovation under contract PID2019-111196RB-I00, called “Development of IoT Systems for People with Disabilities” (Access@IoT), and also was partially funded by the GVA through the AICO/2020/143 project

Reference Models for Digital Manufacturing Platforms

[EN] This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631 and from the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Fraile Gil, F.; Sanchis, R.; Poler, R.; Ortiz Bas, Á. (2019). Reference Models for Digital Manufacturing Platforms. Applied Sciences. 9(20):1-25. https://doi.org/10.3390/app9204433S125920Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286. doi:10.1016/j.compind.2018.05.003Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. doi:10.3390/app9183865Tran, Park, Nguyen, & Hoang. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9(16), 3325. doi:10.3390/app9163325Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201-45218. doi:10.1109/access.2019.2908780Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328-341. doi:10.1016/j.ijinfomgt.2018.07.004Corradi, A., Foschini, L., Giannelli, C., Lazzarini, R., Stefanelli, C., Tortonesi, M., & Virgilli, G. (2019). Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines. IEEE Transactions on Industrial Informatics, 15(2), 1007-1016. doi:10.1109/tii.2018.2867643Gerrikagoitia, J. K., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences, 9(14), 2934. doi:10.3390/app9142934Digital Manufacturing Platforms, Factories 4.0 and beyondhttps://www.effra.eu/digital-manufacturing-platformsZero Defect Manufacturing Platform Project 2019https://www.zdmp.eu/Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0 – An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12. doi:10.1016/j.ifacol.2016.12.002Announcing the IoT Industrie 4.0 Reference Architecturehttps://www.ibm.com/cloud/blog/announcements/iot-industrie-40-reference-architectureVelásquez, N., Estevez, E., & Pesado, P. (2018). Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. Journal of Computer Science and Technology, 18(03), e29. doi:10.24215/16666038.18.e29Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi:10.1016/j.cie.2017.12.029Calvin, T. (1983). Quality Control Techniques for «Zero Defects». IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328. doi:10.1109/tchmt.1983.113617

Approaches for Documentation in Continuous Software Development

It is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading ­– about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions

A model-based framework to bridge architecting, engineering, and testing

The purpose of this project is to construct a model-based framework to bridge architecting, engineering, and testing. The framework supports software development teams in analyzing the architecture of software (Architecture Analysis) and the impacts of changes (Impact Analysis) during software’s development and maintenance.The software, which the project focused on, is one of the controllers composing the printing products. The controller is complex due to its development history, context, and techniques. Its current characteristics have the possibility of causing costly errors and delays in the controller’s evolution. One solution to prevent such risks is formulating an overview of the controller’s architecture.The framework realizes the solution by modeling the controller’s structure and interface definitions. Therefore, the controller development team is able to interpret the controller’s architecture comprehensively, discover the controller’s deficiencies promptly, investigate influences of changes efficiently, and document design decisions regularly

Implementation of DevOps pipeline for Serverless Applications

Serverless computing is a cloud computing execution model where server-side logic runs in the stateless compute containers that are event-triggered and usually fully managed by vendor hosts such as AWS Lambda. This approach is also called Function as a Service (FaaS). Applications that rely on this approach are called Serverless applications. Serverless usage promises infrastructure cost reduction and automatic scalability. One more important benefit of serverless is making the operations part of DevOps process simpler. It reduces the time on the management and maintenance of the servers and sometimes makes them even completely unnecessary. Despite this fact, applications using serverless computing model require a new look at DevOps automation practices since it is a new approach to software architecture design and software development workflow. The goal of this thesis is to implement DevOps pipeline for a Serverless application within a single case organization and evaluate the results of implementation. This is done through design science research, where result artifact is a release pipeline designed and implemented according to the requirements for a new project in the case organization. The result of the study is automated DevOps pipeline with implemented Continuous Integration (CI), Continuous Delivery (CD) and Monitoring practices required for the case project. The research shows that architecture of Serverless applications affects many DevOps automation practices such as test execution, deployment and monitoring of the application. It also affects the decisions about source code repositories structure, mocking libraries and Infrastructure as Code (IaC) tools