Blekinge Institute of Technology
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Paradigm shift on Coding Productivity Using GenAI
No full textGenerative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, an immersive development environment, and automated code evaluation as essential for effective GenAI usage
A roadmap for XR integration in industry : challenges and opportunities
No full textThis paper discusses the current challenges in the development and adoption of Extended Reality (XR) applications in the industry, based on the author’s experience collaborating with various sectors. It identifies knowledge gaps, organizational barriers, and usability issues, describing how these aspects influence one another. Their underlying factors are then analyzed using insights from industrial case studies, revealing possible solutions and emerging opportunities to break and reverse dysfunctional cycles in the XR development roadmap and to maximize the value of XR tools for companies and organizations
A Comparative Study of Federated Learning Methods for Human Activities Recognition in Healthcare
No full textFederated learning (FL) offers a promising solution for human activity recognition (HAR) in healthcare by enabling model training on decentralized data, thereby preserving privacy in compliance with regulations such as GDPR and HIPAA. This study investigates the privacy vs performance trade-offs of FL with centralized machine learning (CML) using the UCI HAR dataset. We focus on three aggregation methods: federated averaging (FedAvg), federated proximal (FedProx), and Krum, under both independent and identically distributed (IID) and non-IID data settings. We evaluate their robustness to poisoning attacks and the impact of local differential privacy (LDP). Our results show that FL outperforms CML in HAR tasks. In non-IID settings, FedAvg achieves up to 97\% accuracy, outperforming FedProx (91\%) and Krum (88\%). Interestingly, non-IID data yields better performance across all methods. While Krum demonstrates strong resilience against poisoning attacks in the absence of LDP, FedProx maintains greater stability when LDP is applied. However, higher privacy levels reduce accuracy to 58–65\%. These findings position FedProx as a balanced option for privacy-preserving healthcare HAR, emphasizing the importance of carefully tuning privacy mechanisms to maintain optimal performance
Hantering av teknisk skuld : Praktiska riktlinjer för identifiering och hantering i svenska myndighetsorganisationer
No full textSwedish government organisations are increasingly undertaking digitalisation initiatives to modernise legacy systems and improve operational quality and efficiency. While these efforts enhance capability and sustainability, they also heighten the risk of unmanaged technical debt (TD), which can lead to long-term financial, operational, and compliance challenges in the public sector. This thesis develops practical Technical Debt Management (TDM) guidelines tailored for secure, on-premises DevOps environments within Swedish government organisations. A qualitative case study was conducted at a Swedish public agency,combining semi-structured interviews with developers, architects, team leaders, and product owners. This was complemented by documentation review and automated analyses of code quality and vulnerabilities. A thematic analysis was used to synthesise findings across technical and organisational domains. The results show that effective TDM requires a combination of technical and governance related measures. Automated code analysis, dependency and vulnerability management, and standardised architectural principles must be complemented by structured documentation, clear ownership, continuous measurement, and cross-role collaboration. Standardisation and integration into CI/CD processes were identifiedas key enablers of long-term maintainability and security. The main contribution of this thesis is a context-specific framework for TechnicalDebt Management that strengthens maintainability, compliance, and sustainabilityin public-sector DevOps environments. The proposed guidelines provide a practical foundation for further refinement and adoption across Swedish governmental digital services.Svenska statliga myndigheter genomför i allt högre grad digitaliseringsinitiativ för att modernisera äldre system och förbättra den operativa kvaliteten och effektiviteten. Även om dessa satsningar stärker förmåga och hållbarhet ökar de samtidigt risken för otryggt hanterad teknisk skuld (TD), vilket kan leda till långsiktiga ekonomiska,operativa och regelefterlevnadsrelaterade utmaningar i offentlig sektor. Denna avhandling utvecklar praktiska riktlinjer för hantering av teknisk skuld(TDM) anpassade för säkra, lokala DevOps-miljöer inom svenska myndigheter. En kvalitativ fallstudie genomfördes vid en svensk offentlig organisation och kombinerade semistrukturerade intervjuer med utvecklare, arkitekter, teamledare och produktägare. Detta kompletterades med dokumentationsgranskning samt automatiserade analyser av kodkvalitet och sårbarheter. En tematisk analys användes för att syntetisera resultaten över tekniska och organisatoriska domäner. Resultaten visar att effektiv hantering av teknisk skuld kräver en kombination av tekniska och styrningsrelaterade åtgärder. Automatiserad kodanalys, hantering av beroenden och sårbarheter samt standardiserade arkitekturprinciper måste kompletteras med strukturerad dokumentation, tydligt ägarskap, kontinuerlig mätning och samarbete mellan roller. Standardisering och integration i CI/CD-processer identifierades som avgörande möjliggörare för långsiktig underhållbarhet och säkerhet. Avhandlingens huvudsakliga bidrag är ett kontextspecifikt ramverk för hantering av teknisk skuld som stärker underhållbarhet, regelefterlevnad och hållbarhet i DevOps-miljöer inom offentlig sektor. De föreslagna riktlinjerna utgör en praktiskgrund för vidare förfining och införande inom svenska myndigheters digitala tjänster
Visually guided extraction of prevalent topics
No full textThe sensemaking process of large sets of text documents is highly challenging for tasks such as obtaining a comprehensive overview or keeping up with the most important trends and topics. Even though several established methods for condensation and summarization of large text corpora exist, many of them lack the ability to account for difference in prevalence between identified topics, which in turn impedes quantitative analysis. In this paper, we therefore propose a novel prevalence-aware method for topic extraction, and show how it can be used to obtain important insights from two text corpora with very different content. We also implemented a prototype visual analytics tool which guides the user in the search for relevant insights and promotes trust in the yielded results. We have verified our application by a user study, as well as by a validation run on a data set with previously known topic structure. The results clearly show that our approach is suitable for text mining, that it can be used by non-experts, and that it offers features which makes it an interesting candidate for use in several different analysis scenarios. Rekrytering 2
What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square
No full textPlazas are an essential pillar of public life in our cities. Historically, they have been seen as public fora, hosting public events that fostered trade, interaction, and debate. However, with the rise of modern urbanism, city planners considered them as part of a larger strategic development scheme overlooking their social importance. As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re‐activate these public spaces to strive for social inclusion and urban resilience. Geometric and urban features of plazas and their surroundings often suggest what kinds of usage the public can make of them. In this project, we explore the application of machine learning to predict the suitability of events in public spaces, aiming to enhance urban plaza design. Learning from traditional urbanism indicators, we consider factors associated with the features of the public space, such as the number of people and the high degree of comfort, which are evolved from three subcategories: external factors, geometric shape, and design factors. We acknowledge that the predictive capability of our model is constrained by a relatively small dataset, comprising 15 real plazas in Madrid augmented digitally to 2025 fictional scenarios through self‐organising maps. The article details the methods to quantify and enumerate quantitative urban features. With a categorical target variable, a classification model is trained to predict the type of event in the urban space. The model is then evaluated locally in Grasshopper by visualising a parametric verified geometry and deploying the model on other existing plazas worldwide regarding geographical proximity to Madrid, where to share or not the same cultural and environmental conditions. Despite these limitations, our findings offer valuable insights into the potential of machine learning in urban planning, suggesting pathways for future research to expand upon this foundational study. © 2025 by the author(s)
The well-being of software engineers : a systematic literature review and a theory
No full textFor decades, software engineering research and practice has focused primarily on technological and process-related factors. Today, there is a growing interest in organizational, social, and psychological factors, including well-being. Organizational studies show that well-being contributes to work outcomes, including creativity, performance, and productivity. But despite its importance, the predictors and outcomes of software engineers' well-being as a multidimensional construct to date are under-researched. This paper reports on the multidimensional well-being of professional software engineers and generates insights for the future research in this area. 44 quantitative survey studies published between 2000 and 2023 were selected and synthesized both quantitatively and qualitatively through a systematic literature review. The results of the review were further analyzed to construct a quantitatively-testable theory, detailing the predictors and outcomes of well-being in software engineering organizations. The total number of research participants included in the selected studies is 16,086 software engineering professionals from at least 42 countries. The literature review identified various measures, constructs, and indicators of well-being, as well as its predictors and outcomes. The theory, based on cumulative results of carefully selected quantitative studies, is an attempt to "correct the record" by establishing well-being in software engineering as a meta-construct of hedonic, eudaimonic, and integrated or hedaimonic qualities predicted by different individual, team and organizational factors and impacting the functioning of software engineers and their organizations. The review highlighted the under-researched aspects of well-being in software engineering and confirmed the need for more advanced quantitative studies. We hope that the theory will benefit researchers in conducting future studies and practitioners in developing nuanced and science-based interventions for improving software engineers' well-being
Ex-post Effectiveness Evaluation of Incentive Regulation in the Electricity Distribution : A Semi-parametric Panel Data StoNED Approach
No full textIn numerous countries, electricity Distribution System Operators (DSOs) function as local monopolies. To counter potential abuse of monopoly power, regulators, especially in Europe, often employ mechanisms like DSO-specific revenue caps to encourage cost reductions among regulated DSOs. Despite its widespread use, literature concerning ex-post evaluation of the effectiveness of revenue cap regulation, particularly divided into its individual components, is lacking. This paper offers two contributions: First, it shows the advantages of utilizing a semi-parametric panel data StoNED framework methodology as a tool for assessing the impact of revenue caps by evaluating the cost efficiency of regulated DSOs in its individual components. Second, the effectiveness of revenue cap regulation is assessed using the Danish DSOs as a case study. The empirical analysis finds evidence that part of the revenue cap incentive scheme appears to promote cost reductions among regulated Danish DSOs.JEL Classification: C14, C23, C51, L43, L51, L94, L9
Personalized smart immersive XR environments : a systematic literature review
No full textIn this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment.
Children’s and adolescents’ perspectives on routine inquiry about violence in specialised outpatient care
No full textObjective This study explores children’s and adolescents’ experiences and opinions of routine inquiries about violence within specialised outpatient care. Utilising a mixed method with a convergent parallel design, the research combines quantitative data from 184 respondents aged 6–17 collected through survey data and qualitative interviews with four participants aged 7–14. The data presented is a byproduct of an ongoing research project that evaluates a questionnaire designed to ask children about violence. Results Findings indicate that most children and adolescents view routine questioning about violence positively or neutrally. The study highlights the importance of healthcare professionals’ responses to disclosures of violence, emphasising that supportive and empathetic reactions can impact children’s willingness to disclose such experiences in the future. The results underscore the necessity for routine inquiries about violence in healthcare settings to ensure that affected children receive appropriate support and intervention