[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs.

[Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of practically relevant research problems in SE. We describe its initial evaluation strategy conducted in a workshop with a network of SE researchers experienced in industry-academia collaboration and report the evaluation of its three assessment criteria (valuable, feasible, and applicable) regarding their importance and completeness in assessing practical relevance.

[Method] We applied LRI retroactively to a published research paper, engaging workshop participants in discussing and assessing the research problem by applying the proposed criteria using a semantic differential scale. Participants provided feedback on the criteria's importance and completeness, drawn from their own experiences in industry-academia collaboration.

[Results] The findings reveal an overall agreement on the importance of the three criteria - valuable (83.3%), feasible (76.2%), and applicable (73.8%) - for aligning research problems with industrial needs. Qualitative feedback suggested adjustments in terminology with a clearer distinction between feasible and applicable, and refinements for valuable by more clearly considering business value, ROI, and originality.

[Conclusion] While LRI still constitutes ongoing research and requires further evaluation, our emerging results strengthen our confidence that the three criteria applied using the semantic differential scale can already help the community assess the practical relevance of SE research problems. 

InnerSource is a collaborative and cost-effective approach to software development that encourages software reuse and co-creation. Many organizations have embraced InnerSource (IS) to reap these benefits by opening their projects for reuse within the organization and enabling contributions from various teams. However, it is crucial to establish a certain level of maturity in IS projects before inviting contributions. This paper introduces IS self-assessment guidelines designed to help IS teams at IKEA (Ingka Group) evaluate the maturity level of their IS projects. We developed the IS self-assessment guidelines following the design science methodology. The focus of the guidelines is to assess how ready IS teams are to receive external contributions. 

Continuous Software Engineering (CSE) is widely adopted in the industry, integrating practices such as Continuous Integration and Continuous Deployment (CI/CD). Beyond technical aspects, CSE also encompasses business activities like continuous planning, budgeting, and operational processes. Coordinating these activities in large-scale product development involves multiple stakeholders, increasing complexity. This study aims to address this complexity by identifying and analyzing critical dependencies in large-scale CSE. Based on 17 semi-structured interviews conducted at two Nordic fintech companies, our preliminary findings indicate that dependencies between software teams and support functions, as well as between software teams and external entities, are the primary sources of delays and bottlenecks. As a next step, we plan to further refine our understanding of critical dependencies in large-scale CSE and explore coordination mechanisms that can better support software development teams in managing these challenges. 

Large Language Models (LLMs) are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications (LIA) expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to identify these threats and suggest mitigations. However, it is a time-consuming practice that requires the involvement of a security practitioner. Our goals are to 1) provide a method for performing threat modeling for LIAs early in their lifecycle, (2) develop a threat modeling tool that integrates existing threat models, and (3) ensure high-quality threat modeling. To achieve the goals, we work in collaboration with our industry partner. Our proposed way of performing threat modeling will benefit industry by requiring fewer security experts' participation and reducing the time spent on this activity. Our proposed tool combines LLMs and Retrieval Augmented Generation (RAG) and uses sources such as existing threat models and application architecture repositories to continuously create and update threat models. We propose to evaluate the tool offline - i.e., using benchmarking - and online with practitioners in the field. We conducted an early evaluation using ChatGPT on a simple LIA and obtained results that encouraged us to proceed with our research efforts.  

På uppdrag av Region Östergötland har Blekinge tekniska högskola och CKS vid Linköpings universitet genomfört en följeforskning av arbetet med att ta fram en rumslig strategi för Östergötland. Syftet har varit att fånga kommunernas reflektioner kring processen och resultatet, samt att identifiera lärdomar och medskick inför framtida regional samverkan. Studien baseras på intervjuer med representanter från 12 av regionens 13 kommuner, genomförda under våren 2025. Intervjuerna visar att kommunerna överlag är positiva till Regionens arbetssätt, som präglats av öppenhet, lyhördhet och en inkluderande process. Särskilt uppskattade är de lokala dialogerna, kunskapsunderlagen och fysiska möten med tillfällen för diskussion. Samtidigt lyfts utmaningar, exempelvis kopplat till resursbrist i mindre kommuner, otydligheter kring strategins kartmaterial samt behovet av tydligare informationsflöden inom samrådsstrukturen. Kommunerna efterfrågar också att det goda samverkansklimatet förs vidare i andra delar av Regionens arbete, och att resultatet av den rumsliga strategin förvaltas långsiktigt och transparent. Rapporten visar att den rumsliga strategin inte bara är ett dokument – utan ett uttryck för en process som skapat ökat förtroende, förståelse och samarbetsvilja mellan Region Östergötland och länets kommuner. Förtroendet är dock sårbart och behöver ständigt vårdas genom tydlig kommunikation och kontinuerlig inkludering.

Spatial planning is commonly conceptualised as a regulatory mechanism for managing competing land uses; however, this perspective tends to obscure how planning actively shapes land use. Responding to calls for relational approaches to land, this article examines how spatial planning enacts mineral extraction through embedded socio-material relations. Drawing on assemblage thinking and a qualitative case study of the Pajala iron ore mine in northern Sweden, the analysis traces three interrelated planning practices: ‘filling and emptying' land, mobilising history and shaping interest and accelerating extraction, which configure land as a site of extraction. The article reveals that planning does not merely manage land but mobilises technical artefacts, historical narratives and urgency claims to enact mineral extraction as a matter of ‘public interest,' while constraining deliberative spaces and depoliticising value conflicts. The article advances a relational and practice-oriented perspective on spatial planning, which challenges essentialist notions of land and highlights planning’s role in reproducing extractive logics under the guise of sustainability. These insights underscore the need for planning scholarship to critically engage with the paradox of the green transition: achieving decarbonisation through intensified mineral extraction while ensuring that spatial planning contributes to more equitable and sustainable trajectories rather than reinforcing extractive imperatives. 

Context: Producers of reusable software assets frequently encounter a high volume of feature requests and bug reports from consumers. InnerSource (IS) offers a potential solution through Participatory Reuse (PR), where consumers participate in the development and maintenance of reusable assets.

Objective: This thesis aims to understand, organize, and improve PR by: 1) understanding the state of the art and practice regarding the costs, benefits, and challenges of software reuse; 2) organizing existing knowledge on PR; and 3) supporting companies in understanding and improving their PR practices.

Methods: This thesis used a mixed-methods approach (an SLR, a case survey, and four case studies) to investigate PR. The SLR explored the reported costs and benefits of software reuse. An exploratory case study assessed reuse practices at a medium-sized company, followed by an improving case study on its readiness for PR. The case survey synthesized existing knowledge into a PR catalog and taxonomy, which was validated and refined through expert evaluations and two additional case studies.

Results: The results of SLR and exploratory case study showed that improved product quality and productivity are the primary benefits of software reuse, though not without associated costs and challenges. The results of two case studies with the same company demonstrated that IS, particularly PR, can help address reuse challenges such as discoverability and ownership of reusable assets. We developed and tested an instrument to assess the company's readiness to adopt PR, identifying areas for improvement and potential solutions. To organize the PR body of knowledge, we developed a PR catalog and taxonomy. The catalog consolidated PR challenges, solutions, and lessons from industrial cases, while the taxonomy provides a mechanism to characterize PR. Finally, we developed a checklist based on the taxonomy for practitioners to assess their current PR practices and identify desired changes.

Conclusion: This thesis advances the field of PR by proposing and validating interventions to improve and characterize PR. The proposed readiness instrument helped the case company to reflect on its current PR practices and identify the areas for improvement. The PR catalog was found to be valuable by experts for providing a clear mapping from PR challenges to the associated solutions and lessons. With the help of two case studies, this thesis demonstrates the utility of the PR taxonomy and its associated checklist in characterizing PR and identifying areas for improvement.

After examining 4247 articles in 10 journals between 2010 and 2020, we found only 11.7% (n = 497) included explicit policy recommendations, and just 1.4% (n = 60) had explicit policy implications subsections. We analysed these 60 articles using behaviourally anchored rating scales (BARS) to assess four key aspects of the policy implications for quality and practicality. Policy implications were generally based on methodologically transparent research, but often lacked rigour in detailing policy and regulatory requirements, and typically demanded significant resources for implementation. Our results contribute to theory by improving our understanding of the why, when and how of policy implications of entrepreneurship research. Also, our BARS framework allows for a differentiation between quality and practical applicability, thereby reframing assumptions by showing that impactful policy implications require both methodological quality and implementation feasibility. Overall, the research-policy gap in entrepreneurship results not merely from disengagement but also research-policy misalignment in content and form.

In the District Heating (DH) sector, the analysis and monitoring of data from DH substations is crucial to keeping the entire DH network running efficiently. Clustering of DH substations based on multivariate data helps analyze their behavior over time. In this context, a visualization-based analysis approach can be particularly beneficial. In this paper, we explore the use of dynamic hypergraph visualization to analyze the clustering results of DH network substations over time. We present the initial results of designing and implementing a visual analytics dashboard that supports DH experts in analyzing different behaviors of DH substations. In the proposed dashboard, we adopt the Parallel Aggregated Ordered Hypergraph (PAOH) technique to visualize dynamic hypergraphs, which provides a compact visualization of multivariate data clustering over time. Moreover, we include additional views with complementary visualizations supporting the analysis and understanding of the dynamic hypergraph main view. We showcase the capability of our dashboard applied on a real DH dataset.

Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low-frequency data, privacy concerns, and battery-constrained gateways. We propose a novel hybrid semi-supervised federated domain adaptation architecture for fault detection in DH. We use a one-class variational autoencoder (VAE) to leverage heterogeneous sensor streams from 434 distributed substations. First, we perform cross-network unsupervised pre-training on multi-sourced data from two independent real-world DH networks, fusing their return temperature dynamics into a robust shared manifold. Second, we leverage maintenance metadata to selectively allow verified-normal clients for per-round fine-tuning of the model. Third, we drastically reduce uplink costs by compressing each client's weight delta using 10% top-k sparsification and demonstrate that our pipeline enables robust few-shot finetuning with 20% of the normal operational data while retaining high detection performance. By strategically training, our method achieves F1 and G-mean scores of up to 97% and an AUC ≥ 99% on real-world DH data. To our knowledge, this is the first work to study cross-domain data fusion in the DH field for fault detection, aiming to enhance and enable effective, scalable, and energy-efficient monitoring of substations.

Diversity, equity and inclusion (DEI) are recognised as essential components of nursing education and clinical practice. The DEI framework aims to broaden knowledge and reduce health disparities. However, the ways in which DEI is applied may vary across national and educational settings due to its context-sensitive nature. The present study aimed to explore how Swedish undergraduate nursing students in their final semester described the phenomenon of DEI within the context of their education. Data were collected through sentence-completion involving 43 participants. The completed sentences were analysed using content analysis, and the study is reported in accordance with COREQ (i.e. Consolidated Criteria for Reporting Qualitative Research) guidelines. Three main categories emerged: 1) Recognising diversity as a foundation of nursing education and practice; 2) Advancing equity in nursing education and person-centred systems of care; and 3) Promoting inclusive practices in nursing education and healthcare delivery. The participating students viewed DEI as interconnected and multifaceted concepts, demonstrating reflective awareness and an ethically grounded understanding of DEI principles as integral to nursing education and practice. The findings highlight the importance of context-sensitive approaches to integrating DEI into nursing education and clinical practice. 

The 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.

Ensuring the quality of input data is essential for building robust and explainable AI models in critical infrastructure domains such as smart grids. However, in SCADA-based intrusion detection pipelines, structural inconsistencies and latent feature drift are rarely assessed. In this preliminary study, we adapt the DQuaG framework—a graph-based reconstruction model originally developed for general tabular data—to assess data quality in a SCADA dataset based on the DNP3 protocol. Weapply the model in an unsupervised setting, using reconstructionloss to detect potential inconsistencies without labeled errors. Our results show that even within clean datasets, structural outliers can be identified, highlighting the value of structure-aware data validation. We discuss the implications for data-centric AI pipelines in SCADA cybersecurity and outline future directions for improving quality assessment and synthetic data generation.

As organizations increasingly integrate digital product development into their core business, structuring the development organization and workflows effectively becomes critical. Yet, there is no one recipe that tells executives how to design their organizations for success. We explore four approaches to organizing product development, drawing on Mintzberg’s organizational structures, and visualizes ways to bridge the gap between software developers and traditional organizational structures. We categorize four organizations along a spectrum from interface-based models to highly integrated product organizations, highlighting key differences in strategic decision-making, autonomy, and cross-functional collaboration. Our findings contribute to the ongoing discussion on large-scale agile transformations and provide practical guidance for navigating the shifts toward a product-centric operational models.

The pandemic experiences of forced work from home (WFH) in the tech industry turned out better than expected. As a result, modern workplaces that employ software engineers have become increasingly hybrid allowing employees to alternate days spent in the office with days spent working remotely. Yet, approaches to regulate the hybrid work arrangements vary. Some companies implemented strict policies with controlled office presence while others rely on recommendations and permit greater locational flexibility. In this paper, we evaluate how different degrees of locational flexibility influence individual work arrangements and satisfaction in two comparative cases: a company with high degree of flexibility (FinCo) and a company with mandatory office presence (TelCo). Through a survey of 547 practitioners, our findings reveal that flexible policies can achieve higher voluntary office attendance than mandatory requirements. To our surprise, we found that the number of employees visiting the office at least 2-3 days per week in the company with greater flexibility was higher (68%) compared to the company with mandatory attendance (58%). The study also identifies key factors influencing work location choices, including commute time and role. The type of tasks and dependencies with colleagues also matter - employees with more WFH days tended to have more individual tasks, while those with more onsite work days engaged in more collaborative tasks and had colleagues who depended on them. Our results suggest that trust-based approaches and creating attractive office environments may be more effective than strict attendance policies in maintaining desired office presence while supporting employee satisfaction. These findings contribute practical insights for organizations seeking to establish effective post-pandemic work policies for software engineers.

This work investigates how prior VR experience influences interactions in a multi-user metaverse-based cultural heritage game. Results show that prior VR experience enhanced usability and control in the VR condition, while participants without prior experience reported greater enjoyment and novelty. Statistical analysis revealed a significant difference in perspicuity between experience levels in the VR condition and in stimulation in the non-VR condition. 

This paper introduces the Gray Wolf Optimized Convolutional Transformer Network, a combined deep learning framework aimed at accurately and efficiently recognizing dynamic hand gestures, especially in American Sign Language (ASL). The model integrates Convolutional Neural Networks (CNNs) for spatial feature extraction, Transformers for temporal sequence modeling, and Grey Wolf Optimization (GWO) for hyperparameter tuning. Extensive experiments were conducted on two benchmark datasets, ASL Alphabet and ASL MNIST to validate the model’s effectiveness in both static and dynamic sign classification. The proposed model achieved superior performance across all key metrics, including a accuracy of 99.40%, F1-score of 99.31%, Matthews Correlation Coefficient (MCC) of 0.988, and Area Under the Curve (AUC) of 0.992, surpassing existing models such as PCA-IGWO, KPCA-IGWO, GWO-CNN, and AEGWO-NET. Real-time gesture detection outputs further demonstrated the model’s robustness in varied environmental conditions and its applicability in assistive communication technologies. Additionally, the integration of GWO not only accelerated convergence but also enhanced generalization by optimally selecting model configurations. The results show that GWO-CTransNet offers a powerful, scalable solution for vision-based sign language recognition systems, combining high accuracy, fast inference, and adaptability in real-world applications. 

Introduction Organ donation and transplantation can be lifesaving. Many donors are older adults. National Donor Registries can promote adherence to patients' rights and safeguard patient integrity if the question about organ donation arises. However, a majority have not entered the National Donor Registry in Sweden.

Objective Investigate how sociodemographic factors, health-related factors, and attitudes toward organ donation are associated with whether or not older adults in Sweden are registered in the National Donor Registry.

Methods A cross-sectional survey design was chosen to explore associations between individual characteristics and registration behavior among older adults. Data was collected through the Swedish National Study on Ageing and Care (SNAC)-IT survey in 2023, a sub-study to the SNAC. 436 participants 60 years old or older answered the survey. The survey included questions on sociodemographic and health-related factors and attitudes toward organ donation. Data was analyzed using descriptive statistics, Spearman's rank correlation, and the Chi-square test.

Results Registration in the Swedish National Donor Registry was low among older adults, with only 25.5% of participants listed despite 70.4% expressing a positive attitude toward organ donation. Among those who oppose organ donation only 6.9% were in the registry. Younger age, higher self-rated health, better health-related quality of life (EQ-5D VAS), being unmarried, being a previous smoker, and having a positive attitude toward donation were all significantly associated with being registered (p < 0.05). These findings demonstrate a gap between supportive beliefs and registration behavior among older adults.

Conclusion There is a gap between beliefs and actions regarding organ donation among older adults. While most have a positive attitude toward organ donation, they are not registered in the National Donor Registry. Additionally, the vast majority who oppose organ donation after death are not registered, raising the risk that their actual wishes may be disregarded due to presumed consent.

Despite numerous initiatives, methodologies and approaches, municipalities and regions continue to face challenges in sustaining long-term efforts toward sustainability. While continuously learning from mistakes and success factors, comprehensive recommendations for how to take a long-term approach for the work, specifically developed for leadership in local and regional contexts, are largely lacking. Therefore, the purpose of this study was to identify a comprehensive set of essential factors that could support long-term sustainability efforts. The study is mainly related to the Framework for Strategic Sustainable Development, but the findings are likely relevant also in relation to other comprehensive sustainability methodologies and approaches. Re-analysis of longitudinal data from action research in Sweden and Finland is supplemented by new qualitative data collection, the study identifies eight conditions for continuity. Being introduced to these conditions upfront may save valuable time and could be utilized as a checklist to be revisited regularly and to strive toward.

Skin cancer, especially melanoma, the most severe type, has increased in recent decades. It develops from cells that grow abnormally and can invade the surrounding tissue and spread throughout the body. Early and accurate diagnosis is essential to prevent disease progression and allow for less invasive clinical treatment. The extraction of complex dermoscopic images and the improvement of lesion classification performance have significantly improved skin cancer diagnosis through the use of convolutional neural networks (CNNs). In this study, a novel deep convolutional neural network that combines ConvNeXt and Vision Transformer (ViT) architectures through an adaptive attention-based approach for advanced feature fusion to automatically multi-classify skin cancer samples. This model is evaluated on two dermoscopy benchmark datasets, including ISIC-2019 and HAM10000 and both datasets reflect the real-world problem of class imbalance. The evaluation results of MedFusionNet are calculated using various evaluation metrics, including accuracy, precision, recall and AUC and compared with deep learning algorithms such as ResNet50, MobileNet V2, DenseNet121 and ViT-B16. The experimental results show that MedFusionNet outperforms the current models with a classification accuracy of 98.80% and 97.90% for HAM10000 and ISIC-2019, respectively. Grad-CAM visualizations qualitatively show that the model focuses on clinically relevant lesion regions, providing interpretive insight without claiming complete causal explainability. The results show that the proposed model can efficiently handle multi-class tasks in dermatological imaging and MedFusionNet is a suitable choice for implementation in real-world computer-aided diagnosis systems.