The applicability of Generative AI in Systematic Literature Reviews : Exploring GPT-4's Role in Automating and Assisting Researchers

This thesis examines the use of Large Language Models (LLMs) to automate key phases of the Systematic Literature Review (SLR) in Software Engineering (SE). Using qualitative interviews and a blind test, we assess the applicability, opportunities, and limitations of LLMs in research workflows.  Study selection (screening) and research identification (search) emerge as the most automatable steps. Repetitive screening is ideal for automation and keyword generation shows potential. However, limitations such as restricted database access and search strategy constraints hinder full automation. Other SLR steps are less suitable for automation. LLMs can reduce human bias, assist in screening, and handle tasks such as formatting, grammar checking, and summarizing.  Despite these benefits, there are concerns about LLM biases, transparency, and ethical issues concerning data privacy. Some question whether automating SLRs supports the fundamental goal of researcher learning. LLM-generated search strings are similar in quality to human-created ones but require manual adjustments for Boolean logic and formatting.  Although LLMs can help, they should not replace human oversight. Cautious automation can enhance, but not replace, traditional research methods. More research is needed to refine the use of LLM in SLRs, focusing on transparency, reliability, and ethics

Children’s and adolescents’ perspectives on routine inquiry about violence in specialised outpatient care

Objective 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

DESIGNING A RESILIENT AUTOMATED WATERBORNE TRASPORT SYSTEM USING DISCRETE EVENT SIMULATION

The paper presents a replicable simulation architecture to assess the economic, environmental, and resilience performance of commercial electric and automated marine passenger transport systems. Built on Discrete Event Simulation (DES), the architecture balances simulation complexity and detail in early system development. It comprises key components such as the modelling framework, data flow, data integration and user interface. A case study in Karlskrona, Sweden, demonstrates its application, showing that DES enhances transparency, facilitates stakeholder communication, and supports iterative solution refinement. The results highlight the architecture’s potential to support sustainable urban waterborne transport and decision-making under uncertainty.RECS – Resilience in electricity & charging system

Language Models to Support Multi-Label Classification of Industrial Data

Background: Multi-label requirements classification is an inherently challenging task, especially when dealing with numerous classes at varying levels of abstraction. The task becomes even more difficult when a limited number of requirements is available to train a supervised classifier.  Zero-shot learning does not require training data and can potentially address this problem. Objective: This paper investigates the performance of zero-shot classifiers on a multi-label industrial dataset. The study focuses on classifying requirements according to a hierarchical taxonomy designed to support requirements tracing. Method: We compare multiple variants of zero-shot classifiers using different embeddings, including 9 language models (LMs) with a reduced number of parameters (up to 3B), e.g., BERT, and 5 large LMs (LLMs) with a large number of parameters (up to 70B), e.g., Llama. Our ground truth includes 377 requirements and 1968 labels from 6 output spaces. For the evaluation, we adopt traditional metrics, i.e., precision, recall, $F_1$, and $F_\beta$, as well as a novel label distance metric $D_n$. This aims to better capture the classification's hierarchical nature and to provide a more nuanced evaluation of how far the results are from the ground truth. Results: 1) The top-performing model on 5 out of 6 output spaces is T5-xl, with maximum  $F_\beta = 0.78$ and $D_n = 0.04$, while BERT base outperformed the other models in one case, with maximum $F_\beta = 0.83$ and $D_n = 0.04$. 2) LMs with smaller parameter size produce the best classification results compared to LLMs. Thus, addressing the problem in practice is feasible as limited computing power is needed. 3) The model architecture (autoencoding, autoregression, and sentence-to-sentence) significantly affects the classifier's performance. Contribution: We conclude that using zero-shot learning for multi-label requirements classification offers promising results. We also present a novel metric that can be used to select the top-performing model for this problem

Palliativa och svårt sjuka patienters tankar kring dödshjälp

Bakgrund: Många palliativa och svårt sjuka patienter uppger idag att de förlorar sin värdighet och autonomi när de drabbas av svår och livshotande sjukdom. Både sjuksköterskor och patienter beskriver att det är viktigt för patienten att få behålla sin autonomi och att själva bestämma hur, var och när de ska dö. Många palliativa och svårt sjuka patienter som lider av svår smärta eller förlorad autonomi uppger att det finns en önskan om dödshjälp. Syfte: Att belysa palliativa och svårt sjuka patienters tankar kring dödshjälp. Metod: En kvalitativ litteraturöversikt där kvalitativa och kvantitativa artiklar använts. Resultat: Fyra teman i resultatet var återkommande. Det återfanns en önskan av avslut, rädsla, känslan av hopplöshet och smärta. Många palliativa och svårt sjuka patienter uppgav att det fanns en önskan om att få välja dödshjälp när de drabbats av en obotlig eller svår sjukdom. Många patienter uppgav att en stor anledning till att de vill ha möjlighet att välja dödshjälp grundade sig i att de förlorade sin värdighet och autonomi, men också många gånger på grund av svåra smärtor. Att befinna sig i livets slutskede kan leda till många jobbiga tankar och funderingar, bland annat känslan av hopplöshet och rädsla, det är något sjuksköterskan måste ha resurser och kunskap för att kunna bemöta. Slutsats: Det finns ett större behov av en ökad förståelse och öppen dialog kring dödshjälp. Många patienter som lider av svår sjukdom önskar att ha tillgång till dödshjälp då sjukdomen påverkat deras autonomi och värdighet. Sjuksköterskan måste också få mer hjälpmedel för att kunna hantera bemötandet av dessa patienter. Ökad kunskap och mer forskning och studier är nödvändigt för att göra bemötande och omvårdnad bättre

Implementing Industry 4.0 in Production Planning and Control : A study of Organizational Challenges and Expectations in a Project-Oriented Manufacturing Context

 The integration of Industry 4.0 technologies is transforming industrial operations. In Production Planning and Control (PPC), this digital shift introduces both opportunities and challenges, especially in project-based and complex production environments.This study aims to explore how the implementation of digital tools affects PPC in a complex and project-based industrial setting, with a specific focus on decision-making, required competencies, and organizational challenges. A qualitative case study was conducted at NKT in Karlskrona, Sweden, involving six semi-structured interviews with professionals working in PPC and digitalization. The collected data was analyzed thematically from a socio-technical systems perspective.The findings highlight five main challenges to digital implementation: limited system trust, difficulty integrating soft values, unclear responsibility, insufficient training, and resistance to change. Six key competencies emerged as needed for effective PPC in a digital environment: analytical skills, process knowledge, collaboration, adaptability, IT and data literacy, and system-specific knowledge. Furthermore, five decision-making factors were identified: shared responsibility structures, time-sensitive complexity, top-down directives, the need for structured data, and the combination of intuition and data. In conclusion, not only technical implementation is required for a successful digitalization of PPC, but also organizational alignment, competence development, and trust building. This study contributes to a deeper understanding of the socio-technical interplay in PPC and offers practical implications for managing digital transitions in complex production environments. Integrationen av Industri 4.0-teknologier håller på att förvandla industriella verksamheter. Inom produktionsplanering och -styrning (PPC) innebär detta digitala skifte både möjligheter och utmaningar, särskilt i projektbaserade och komplexa produktionsmiljöer.Denna studie syftar till att undersöka hur implementeringen av digitala verktyg påverkar PPC i en komplex, projektbaserad industrikontext, med särskilt fokus på beslutsfattande, nödvändiga kompetenser och organisatoriska utmaningar.En kvalitativ fallstudie genomfördes på NKT i Karlskrona, Sverige, där sex semistrukturerade intervjuer hölls med yrkesverksamma inom PPC och digitalisering. Det insamlade materialet analyserades tematiskt med ett sociotekniskt systemperspektiv.Studien identifierade fem huvudsakliga utmaningar för digital implementering: begränsat systemförtroende, svårigheter att integrera mjuka värden, oklara ansvarsförhållanden, bristande introduktion och utbildning samt förändringsmotstånd. Sex viktiga kompetenser lyftes fram som avgörande i en digitaliserad PPC-miljö: analytisk förmåga, process- och domänkunskap, samarbets- och kommunikationsförmåga, anpassningsförmåga, grundläggande IT- och datakunskap samt systemspecifik kunskap. Dessutom identifierades fem viktiga faktorer som påverkar beslutsfattande i en digital miljö: delat ansvar, tidspressad komplexitet, toppstyrning, behov av strukturerade dataformat samt samspelet mellan intuition och data.Slutsatsen visade att för en lyckad digitalisering av PPC krävs mer än teknisk implementering; organisatorisk anpassning, kompetensutveckling och förtroendeskapande är avgörande. Studien bidrar till ökad förståelse för det sociotekniska samspelet inom PPC och erbjuder praktiska insikter för att hantera digitala övergångar i komplexa produktionsmiljöer

Neural-XGBoost : A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies.

Revisiting early city planning competitions : Per O. Hallman and the art of city building in Helsinki-Töölö and Gothenburg

At the turn of the twentieth century, the young Swedish architect Per O. Hallman emerged as an advocate for artistic city planning approaches. Inspired by St & auml;dtebau philosophies, Hallman viewed city planning competitions as crucial platforms to advance these innovative ideas, advocating for them as a counterpoint to the prevailing chessboard planning methods driven by engineers and land surveyors. His ideas resonated beyond Sweden, also diffusing into Finnish professional circles. This resulted in competitions for the unplanned districts of T & ouml;& ouml;l & ouml; in Helsinki (1898-1902) and southern Gothenburg (1901) - the first occurrences in Finland and Sweden to adopt artistic principles. Although Hallman served as a key agent for diffusing Camillo Sitte's principles from the Continent, his contributions remain underexplored within international city planning scholarship. The article addresses this gap by examining Hallman's largely untapped archival materials, which shed new light on his cross-collaborations with other prominent figures, including Fredrik Sundb & auml;rg, Bertel Jung, Lars Sonck, and even Josef St & uuml;bben, with whom he worked closely and judged the second stage of the T & ouml;& ouml;l & ouml; competition. Through these collaborations, Hallman not only advanced his own planning approach but also facilitated the breakthrough and integration of artistic values into city planning across two of the Nordic countries

Towards Reliable Eager Test Detection : Practitioner Validation and a Tool Prototype

Context: Existing tools for detecting eager tests produce many false positives, rendering them unreliable for practitioners. To address this, our previous work introduced a novel definition of the Eager Test smell and a heuristic for more effective identification. Comparing the heuristic’s results with existing detection rules revealed eight test patterns where the rules misclassified the presence or absence of eager tests. Objective: We aim to gather practitioners’ feedback on our heuristic’s assessment of these eight test patterns and operationalize the heuristic in a tool we named EagerID. Method: We conducted a survey to collect practitioners’ feedback on the eight identified test patterns and developed EagerID to detect eager tests in Java unit test cases using JUnit. We also preliminarily evaluated EagerID on 300 test cases, which were manually analyzed in our previous study. Results: Our survey received 23 responses from practitioners with a wide range of experience. We found that most practitioners agreed with the assessment of our heuristic. Furthermore, the preliminary evaluation of EagerID returned high precision (100%), recall (91.76%), and F-Score (95.70%). Conclusion: Our survey findings highlight the practical relevance of the heuristic. The preliminary evaluation of the EagerID tool confirmed the heuristic’s potential for automation. These findings suggest that the heuristic provides a solid foundation for both manual and automated detection

Enhancing Peak-Hour Connectivity in Urban Ride-Sharing Platforms through Dynamic Graph Theory Analysis : A Simulation-Based Approach

This thesis explores the application of dynamic graph theory to optimize urban ridesharing platforms, particularly during peak-hour traffic congestion. By integrating real-time traffic data with dynamic routing algorithms, this research aims to improve ride-sharing efficiency, reduce congestion, and enhance urban mobility. The study investigates the effectiveness of combining Dijkstra’s and A* algorithms, focusing on the dynamic adjustment of routes based on real-time traffic conditions.  Further, the study leverages a simulation of the city of Gothenburg, Sweden’s road network, this the result from this demonstrates that the combination of these algorithms significantly reduces travel time and congestion compared to traditional static routing methods. The results of this study contribute to the development of more efficient, adaptive, and scalable ride-sharing systems, with implications for urban transportation planning and policy.  The findings emphasize the importance of integrating real-time traffic data into ride-sharing platforms to improve service delivery and reduce congestion during peak hours