Blekinge Institute of Technology
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Monolithic-to-Microservices Migration Processes : An Interview Study of Migration Processes According to Practical Experience
No full textBackground. Software evolution of enterprise systems is a critical aspect of modern software engineering. Most of the costs in software lifecome from evolution and maintenance, scalability and maintainabilityemerge as critical quality attributes. Many enterprise applications areoften built on technology stacks that have reached the end of their lifecycles or with architectural shortcomings that hinder flexibility andgrowth. Microservices architecture arose to address these issues. Objectives. The aim of this thesis is to obtain detailed and qualitative data with the practitioners’ narratives based on their experiencewith migration processes from MA to MSA and to compare the datagathered with migration processes that are proposed in the literature. Methods. The research was divided into 3 parts. A literature reviewto search for publications that propose processes for migrating frommonolithic architectures to microservice architectures, so that we canstudy those processes. Followed by a qualitative interview to get thepractitioners narrative on the proposed steps for a migration foundin the literature review. And a thematic analysis to process the datacollected from the interviews to then compare it with the steps foundin the literature review. Results. The literature review produced 6 migration processes thatmet our requirements. This factorization of the processes into constituent steps informed the design of the interview. The thematicanalysis done on the resulting dataset generated 14 major themes and41 sub-themes on the subject of MA-to-MSA migrations. The datasetincluded validation ratings for each migration step, as well as scoresfor the practitioners’ migration process in regard of achieved maintainability, scalability, and overall effectiveness of the migration. Conclusions. This study contributes to evaluate migration processesfound in the literature that migrates from monolithic architecture tomicroservices architecture based on the practitioners narrative. It results into a validation of every migration step found ending into anon-crucial, nice to have or must-have grading. That grading and thethematic analysis will be the justification and answer to our researchobjectives. The thesis will also propose some future work that can bebased on the findings of this stud
An overview of cyber attacks on critical cyber-physical systems and government infrastructures
No full textThis study aimed to analyze the nature, scale, and consequences of cyberattacks on critical cyber-physical systems in Ukraine over the past decade, using a methodology based on classifying attacks by type, threat actor (including Russian hacking groups Sandworm, Fancy Bear, and Ember Bear responsible for half of the 22 analyzed incidents), target sector, and temporal patterns. It also included comparative analysis of cyber defense strategies. The Chinese group Volt Typhoon also demonstrated high risk through living-off-the-land techniques. While phishing remained the primary attack vector (7 cases), sophisticated supply chain attacks like NotPetya caused significant damage, with the energy sector being most targeted (7 incidents) due to its strategic importance. Six attacks involved manipulation of Industrial Control Systems/Operational Technology protocols, while four employed destructive wiper malwares. The Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege (STRIDE) analysis of digital platforms concluded that modern challenges require innovative solutions like Cybersecurity Mesh Architecture, digital immunity systems, and artificial intelligence, along with international coordination, while addressing barriers such as legacy systems, workforce shortages, and regulatory fragmentation, ultimately providing an evidence base for improving cybersecurity strategies at national and international levels.
Satellite-Based Rainfall Datasets : A Global Systematic Review of Applications, Accuracy, and Research Gaps
No full textIn the context of increasing climate variability and the gradual decline of ground-based observation networks, satellite-based rainfall estimates (SREs) have become indispensable tools for hydrological monitoring disaster preparedness, and climate modeling. Satellite technology has evolved rapidly in recent years, with new missions, sensors, and techniques implemented by agencies and researchers to improve SRE products. This study presents a global systematic review explicitly applying the PRISMA methodology, offering a structured and reproducible framework for evidence synthesis. It evaluates the performance of the most widely used SREs across all continents from January 2018 to November 2025, with particular emphasis on their application in data-scarce and hydrologically complex regions. Drawing from 636 peer-reviewed studies, the review identifies key factors affecting the accuracy of SREs, including topography, rainfall type, and seasonality. Notably, products that integrate satellite data with ground-based observations consistently demonstrate superior performance compared to satellite-only estimates. Among them, IMERG-Final and CHIRPS stand out as the most widely used datasets worldwide, with IMERG-Final showing particularly promising performance across most continents. The findings highlight the need for future research to prioritize the development of advanced bias correction algorithms, region-specific calibration methods, and hybrid models that incorporate additional meteorological variables. Although previous reviews have addressed this approach, the present synthesis offers an updated and concise reference for selecting suitable SREs across diverse environmental and operational contexts.
Cards for Alternative Research Design (CARD) : Refining and Evolving a Research Knowledge Development Activity for Computer Science Education
No full textOne of the most important choices a researcher makes is selecting a research paradigm and methodology, without which they will be hampered in their search for knowledge and answers. Ideally, researchers consider all possible approaches and select the most appropriate one, but several factors constrain this: Time, familiarity with certain approaches, and the uncertainty of the benefit of change. \Cer draws from many research disciplines, exposing new possibilities that may not be seized due to these limitations. The Cards for Alternative Research Design (CARD) deck is designed to expand researchers awareness of different research approaches through a card-based prototyping exercise. This serious card-based game approach could be used by graduate students, early-career researchers, research course instructors, research mentors, and even experienced researchers. CARD games are intended to reduce the formality and potentially confrontational aspects of being asked to consider new approaches, allowing participants to examine their current research and plans through different paradigms, methodologies, and constraints, without it being a direct criticism of their current choices. This can increase the level of understanding of research framing and practice, strengthening the arguments for using a given approach and introducing valid arguments to adopt different approaches, with low time investment. This report summarizes the current evolution of the CARD deck, including an accompanying glossary and multiple games that can be played with the cards.
Healthcare organizations in entrepreneurial ecosystems : an integrative framework and future research agenda
No full textEntrepreneurial ecosystems as lenses promoting innovation and entrepreneurship have attracted substantial interest in research. However attention towards the role of sector-specific actors, such as healthcare-related organizations, within entrepreneurial ecosystems remains underexplored. This paper investigates how healthcare-related organizations are conceptualized within entrepreneurial ecosystem research through an integrative review of the literature addressing EEs and healthcare. The findings show that healthcare entrepreneurs are often portrayed as actors addressing both commercial and public health purposes. Hospitals are perceived as laboratories of collaboration and learning, combining care delivery with innovation-oriented activities, while life sciences are seen as critical to regional innovation and development by attracting scientific knowledge and enabling translation into entrepreneurial opportunities. Institutional and resource dynamics are also perceived as important for healthcare and entrepreneurship ecosystems, through facilitation of resource mobilization and regulatory adaptation. Based on this review, the paper develops a framework that positions healthcare organizations within entrepreneurial ecosystems according to their degree of hybridity and ecosystem roles. This research contributes to EE literature by examining entrepreneurial ecosystems research through a sector-sensitive and relational perspective. The paper offers a framework and research agenda calling for more exploration of institutional complexity, patient-centred innovation, the evolving role of hospitals, and hybrid governance models in entrepreneurial ecosystems.
Integrating pair programming as a work practice
No full textContext: Pair programming (PP) is more relevant than ever. As modern systems grow in complexity, knowledge sharing and collaboration across teams have become essential. However, despite well-documented benefits of PP, its adoption remains inconsistent across software teams. Objective: This study aims to understand the factors that facilitate or hinder team members' adoption as well as lasting engagement in PP. Method: We have conducted an exploratory single-case study in a mature agile company in Norway. We collected data through two rounds of interviews with team members in different roles and performed a thematic analysis of the interviews. Results: Our key finding is that multiple factors, related to the perceptions of how PP contributes to daily work, social dynamics and learning, efforts associated with engaging in PP sessions, company and team attitudes, resources, infrastructure, and task characteristics, affect PP engagement. Conclusion: Long-term engagement in PP requires expected benefits with the practice being confirmed in firsthand experiences. Adapting the practice to each unique team, with insights drawn from collective learning, is also beneficial. Our findings will be beneficial for software practitioners seeking to make PP an integrated part of their team's workflow.Vertical sustainable platform organizations (VertiGO
Architectural Selection Framework for Synthetic Network Traffic : Quantifying the Fidelity–Utility Trade-off
No full textThe fidelity and utility of synthetic network traffic are critically compromised by architectural mismatch across heterogeneous network datasets and prevalent scalability failure. This study addresses this challenge by establishing an Architectural Selection Framework that empirically quantifies how data structure compatibility dictates the optimal fidelity-utility trade-off. We systematically evaluate twelve generative architectures (both non-AI and AI) across two distinct data structure types: categorical-heavy NSL-KDD and continuous-flow-heavy CIC-IDS2017. Fidelity is rigorously assessed through three structural metrics (Data Structure, Correlation, and Probability Distribution Difference) to confirm structural realism before evaluating downstream utility. Our results, confirmed over twenty independent runs (N = 20), demonstrate that GAN-based models (CTGAN, CopulaGAN) exhibit superior architectural robustness, consistently achieving the optimal balance of statistical fidelity and practical utility. Conversely, the framework exposes critical failure modes, i.e., statistical methods compromise structural fidelity for utility(Compromised fidelity), and modern iterative architectures, such as Diffusion Models, face prohibitive computational barriers, rendering them impractical for large-scale security deployment. This contribution provides security practitioners with an evidence-based guide for mitigating architectural failures, thereby setting a benchmark for reliable and scalable synthetic data deployment in adaptive security solutions
Institutions, resilience, and intrapreneurship in healthcare : a critical literature review
No full textIn this paper, we investigate how institutional conditions shape individual-level resilience in healthcare organizations which in turn facilitates the intrapreneurial behavior of healthcare professionals. Using a critical literature review methodology, we identify that the current literature centers around three dominant clusters defined by intrapreneurial outcomes: organizational performance, innovation and knowledge creation, and intrapreneurial agency. Across these clusters, both formal and informal institutions play a complementary role in shaping resilience. This resilience, enables professionals to navigate uncertainty, cope with institutional barriers, and drive bottom-up change within hierarchical healthcare organizations, fostering intrapreneurship predominantly related to internal innovation and organizational improvement. Based on these insights, we propose a conceptual model illustrating how institutions jointly foster resilience, which acts as a mediating mechanism between institutional conditions and intrapreneurial behavior in healthcare. The study offers theoretical contributions to research on intrapreneurship and resilience, as well as managerial implications for healthcare leaders and policymakers.GRINS - Growing Resilient, INclusive and Sustainabl
A Learnable Cross-Modal Adapter for Industrial Fault Detection Using Pretrained Vision Models
No full textAutomatic fault detection and diagnosis (FDD) are critical for maintaining reliable and efficient industrial systems. However, conventional methods rely heavily on manual inspections or threshold-based techniques, which often fail to capture the dynamic patterns in time series (TS) sensor data. As a result, faults persist for extended periods, leading to suboptimal system operations, increased energy waste, and significant economic losses. This work proposes a cross-modal framework that facilitates the efficient deployment of state-of-the-art pretrained vision models for enhanced FDD, with two novel TS-to-image transformations: first, an adapter deep encoder that learns optimal, task-specific representations from raw sensor data while generating outputs that are input-compliant with pretrained models. Second, an enhanced line plot that creates geometric shapes of two related signals. Comparative experiments against fixed methods, including spectrograms, Gramian angular fields, Markov transition fields, recurrence plots, and five deep learning baseline models, showed substantial performance gains across diverse domains. InceptionTime achieved the highest average baseline performance with an F<inf>1</inf> of 88.6%, while the adapter and shapes achieved 94.4% and 92.4%, respectively. The findings highlight the potential of the cross-modal framework for FDD to facilitate early intervention and efficient system maintenance in industrial settings
Traceability in DevOps : Expectations, Challenges and Opportunities
No full textBackground: DevOps practices have become central to modern software development, enabling rapid delivery through continuous integration and deployment. At the same time, software traceability remains important for accountability, coordination, compliance, and quality assurance. However, maintaining traceability in fast-paced DevOps environments is challenging, and existing research has largely focused on plan-driven development or technical solutions without fully capturing current industry practice. Objectives: The objective of this thesis is to understand how software traceability is perceived, implemented, and sustained in contemporary DevOps environments. The study investigates current practices, challenges, and perceived impacts of trace ability on software quality, release cycles, and collaboration, as well as the role of tooling and automation in supporting traceability. Five research questions address: (1) current perception and implementation, (2) challenges in integration, (3) impact on delivery outcomes, (4) critical success factors for tool integration, and (5) the role of automation in feedback loops. Methods: A sequential mixed-methods approach was adopted. First, an online survey was conducted with 90 practitioners working in DevOps-related roles to capture broad patterns and perceptions. This was followed by 20 semi-structured interviews to explore experiences, workflows, and contextual factors in greater depth. Quantitative data were analysed using descriptive statistics, while qualitative data were analysed through thematic analysis. Results: The results show that traceability is widely regarded as important in DevOps, but is only partially and unevenly implemented in practice. Key challenges include high maintenance effort, fragmented toolchains, and inconsistent adoption across teams. Traceability is perceived to have a positive impact on software quality, collaboration, and compliance, while its effect on release speed is more mixed. Automation is seen as an important enabler, but not a complete solution, as effective traceability also depends on clear ownership and alignment with everyday work practices. Conclusions: This thesis concludes that traceability in DevOps should be understood as a socio-technical practice rather than a purely technical feature. Sustainable traceability requires a balance between speed and discipline, supported by integrated tools, lightweight automation, and organisational commitment. The findings provide empirical insights that can inform both future research and practical improvements in DevOps environments