Architectural Support for Software Performance in Continuous Software Engineering: A Systematic Mapping Study

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future researc

Managing Future Challenges for Safety

This open access book addresses the future of work and industry by 2040—a core interest for many disciplines inspiring a strong momentum for employment and training within the industrial world. The future of industrial safety in terms of technological risk-management, although of obvious concern to international actors in various industries, has been quite sparsely addressed. This brief reflects the viewpoints of experts who come from different academic disciplines and various sectors such as oil and gas, energy, transportation, and the digital and even the military worlds, as expressed in debates and discussions during a two-day international seminar. The contributors address such questions as: What influence will ageing and lack of digital skills in the workforce of the occidental world have on safety culture? What are the likely impacts of big data, artificial intelligence and autonomous technologies on decision-making, and on the roles and responsibilities of individual actors and whole organizations? What role have human beings in a world of accelerating changes? What effects will societal concerns and the entrance of new players have on technological risk management and governance? Managing Future Challenges for Safety will interest and influence researchers considering the future effects of a number of currently developing technologies and their practitioner counterparts working in industry and regulation

Towards Optimization of Anomaly Detection Using Autonomous Monitors in DevOps

Continuous practices including continuous integration, continuous testing, and continuous deployment are foundations of many software development initiatives. Another very popular industrial concept, DevOps, promotes automation, collaboration, and monitoring, to even more empower development processes. The scope of this thesis is on continuous monitoring and the data collected through continuous measurement in operations as it may carry very valuable details on the health of the software system. Aim: We aim to explore and improve existing solutions for managing monitoring data in operations, instantiated in the specific industry context. Specifically, we collaborated with a Swedish company responsible for ticket management and sales in public transportation to identify challenges in the information flow from operations to development and explore approaches for improved data management inspired by state-of-the-art machine learning (ML) solutions.Research approach: Our research activities span from practice to theory and from problem to solution domain, including problem conceptualization, solution design, instantiation, and empirical validation. This complies with the main principles of the design science paradigm mainly used to frame problem-driven studies aiming to improve specific areas of practice. Results: We present identified problem instances in the case company considering the general goal of better incorporating feedback from operations to development and corresponding solution design for reducing information overflow, e.g. alert flooding, by introducing a new element, a smart filter, in the feedback loop. Therefore, we propose a simpler version of the solution design based on ML decision rules as well as a more advanced deep learning (DL) alternative. We have implemented and partially evaluated the former solution design while we present the plan for implementation and optimization of the DL version of the smart filter, as a kind of autonomous monitor. Conclusion: We propose using a smart filter to tighten and improve feedback from operations to development. The smart filter utilizes operations data to discover anomalies and timely report alerts on strange and unusual system's behavior. Full-scale implementation and empirical evaluation of the smart filter based on the DL solution will be carried out in future work

DevOps and software quality : a systematic mapping

Quality pressure is one of the factors affecting processes for software development in its various stages. DevOps is one of the proposed solutions to such pressure. The primary focus of DevOps is to increase the deployment speed, frequency and quality. DevOps is a mixture of different developments and operations to its multitudinous ramifications in software development industries, DevOps have attracted the interest of many researchers. There are considerable literature surveys on this critical innovation in software development, yet, little attention has been given to DevOps impact on software quality. This research is aimed at analyzing the implications of DevOps features on software quality. DevOps can also be referred to a change in organization cultures aimed at removal of gaps between the development and operations of an organization. The adoption of DevOps in an organization provides many benefits including quality but also brings challenges to an organization. This study presents systematic mapping of the impact of DevOps on software quality. The results of this study provide a better understanding of DevOps on software quality for both professionals and researchers working in this area. The study shows research was mainly focused in automation, culture, continuous delivery, fast feedback of DevOps. There is need of further research in many areas of DevOps (for instance: measurement, development of metrics of different stages to assess its performance, culture, practices toward ensuring quality assurance, and quality factors such as usability, efficiency, software maintainability and portability). Keywords: DevOps, development, operations, software, software quality, automation, measurement, systematic mappingpublishedVersio

Harnessing the Potential of Blockchain in DevOps: A Framework for Distributed Integration and Development

As the use of DevOps practices continues to grow, organizations are seeking ways to improve collaboration, speed up development cycles, and increase security, transparency, and traceability. Blockchain technology has the potential to support these goals by providing a secure, decentralized platform for distributed integration and development. In this paper, we propose a framework for distributed DevOps that utilizes the benefits of blockchain technology that can eliminate the shortcomings of DevOps. We demonstrate the feasibility and potential benefits of the proposed framework that involves developing and deploying applications in a distributed environment. We present a benchmark result demonstrating the effectiveness of our framework in a real-world scenario, highlighting its ability to improve collaboration, reduce costs, and enhance the security of the DevOps pipeline. Conclusively, our research contributes to the growing body of literature on the intersection of blockchain and DevOps, providing a practical framework for organizations looking to leverage blockchain technology to improve their development processes.Comment: pages 10, figures

Maturity model for DevOps

Businesses today need to respond to customer needs at unprecedented speed. Driven by this need for speed, many companies are rushing to the DevOps movement. DevOps, the combination of Development and Operations, is a new way of thinking in the software engineering domain that recently received much attention. Since DevOps has recently been introduced as a new term and novel concept, no common understanding of what it means has yet been achieved. Therefore, the definitions of DevOps often are only a part relevant to the concept. When further observing DevOps, it could be seen as a movement, but is still young and not yet formally defined. Also, no adoption models or fine-grained maturity models showing what to consider to adopt DevOps and how to mature it were identified. As a consequence, this research attempted to fill these gaps and consequently brought forward a Systematic Literature Review to identify the determining factors contributing to the implementation of DevOps, including the main capabilities and areas with which it evolves. This resulted in a list of practices per area and capability that was used in the interviews with DevOps practitioners that, with their experience, contributed to define the maturity of those DevOps practices. This combination of factors was used to construct a DevOps maturity model showing the areas and capabilities to be taken into account in the adoption and maturation of DevOps.Hoje em dia, as empresas precisam de responder às necessidades dos clientes a uma velocidade sem precedentes. Impulsionadas por esta necessidade de velocidade, muitas empresas apressam-se para o movimento DevOps. O DevOps, a combinação de Desenvolvimento e Operações, é uma nova maneira de pensar no domínio da engenharia de software que recentemente recebeu muita atenção. Desde que o DevOps foi introduzido como um novo termo e um novo conceito, ainda não foi alcançado um entendimento comum do que significa. Portanto, as definições do DevOps geralmente são apenas uma parte relevante para o conceito. Ao observar o DevOps, o fenómeno aborda questões culturais e técnicas para obter uma produção mais rápida de software, tem um âmbito amplo e pode ser visto como um movimento, mas ainda é jovem e ainda não está formalmente definido. Além disso, não foram identificados modelos de adoção ou modelos de maturidade refinados que mostrem o que considerar para adotar o DevOps e como fazê-lo crescer. Como consequência, esta pesquisa tentou preencher essas lacunas e, consequentemente, apresentou uma Revisão sistemática da literatura para identificar os fatores determinantes que contribuem para a implementação de DevOps, incluindo os principais recursos e áreas com os quais ele evolui. Isto resultou numa lista de práticas por área e por capacidade, que foi utilizado como base nas entrevistas realizadas com peritos em DevOps que, com a sua experiência, ajudaram a atribuir níveis de maturidade a cada prática. Esta combinação de fatores foi usada para construir um modelo de maturidade de DevOps mostrando as áreas e as capacidades a serem levados em consideração na sua adoção e maturação