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

On the road to interactive LLM-based systematic mapping studies

Context: The research volume is continuously increasing. Manual analysis of large topic scopes and continuously updating literature studies with the newest research results is effort intensive and, therefore, difficult to achieve. Objective: To discuss possibilities and next steps for using LLMs (e.g., GPT-4) in the mapping study process. Method: The research can be classified as a solution proposal. The solution was iteratively designed and discussed among the authors based on their experience with LLMs and literature reviews. Results: We propose strategies for the mapping process, outlining the use of agents and prompting strategies for each step. Conclusion: Given the potential of LLMs in literature studies, we should work on a holistic solutions for LLM-supported mapping studies.

Software Analytics for Supporting Practitioners in Bug Management

Context: In large-scale software development, a large number of bug reports are submitted during software development and maintenance. Practitioners need the ability to analyze this abundant data to make data-driven decisions about bug management tasks. Objective: This thesis aims to utilize software analytics (SA) to support practitioners in bug management. The objectives of this thesis are (1) to identify and structure the knowledge on the use of SA for software engineering (SE) tasks and (2) to investigate and evaluate the practical application of SA to support practitioners in managing invalid bug reports (IBRs). Method: We conducted a tertiary review and systematic mapping study to achieve the first objective and comparative experiments and two industrial case studies to achieve the second objective. Throughout the thesis work, we relied on a technology transfer model to guide the research and facilitate the adoption of ML techniques for the early identification of IBRs at the case company. Results: We provide a comprehensive map of various SA applications for SE tasks and a decision matrix that can assist in selecting the most appropriate ML technique for bug report classification for a given context. Our results indicate that an ML technique can identify IBRs with acceptable accuracy at an early stage in practice. Furthermore, the results of an SA-based approach indicate that it can support practitioners in devising preventive measures for IBRs. Conclusion: Through industrial validations, this thesis provides evidence of the usefulness of SA in bug management, particularly in supporting practitioners in managing IBRs in large-scale software development

Exploring the Relationship between Test Smells and Code Smells

Background: In software evolution, code smells and test smells represent design f laws that can negatively impact the maintainability, readability, and reliability of both production and test code. These smells, although not immediately causing defects, can lead to long-term challenges such as increased technical debt, difficulty in debugging, and higher maintenance costs. This research focuses on the survivability of code and test smells, examining how long they persist in the codebase and their impact on software quality. Additionally, it explores the associations between code and test smells, providing insights into how the presence of one may indicate the other, and helping developers prioritise which smells to refactor based on their severity and longevity.  Objectives : The primary objective of this research is to find the survivability of Smells (Code Smells and Test Smells) and to find any possible associations between the smells code smells occurring in production code and test smells occurring in associated test code.  Methods: We conducted archival analysis on five repositories selected from GitHub. To find the survivability of smells we mine the selected repositories using the tool DesigniteJava to detect the smell and find their survivability. We also performed association rule mining to detect any associations with a minimum confidence of 50% between code smells and test smells.  Results:We found that test smells tend to survive longer than code smells. Among code smells, Feature Envy has the lowest survivability, while Magic Number exhibits the highest. For test smells, Ignored Test shows the lowest survivability, whereas Assertion Roulette has the highest. Additionally, we observed that the Long Statement smell in production code is frequently associated with Conditional Test Logic and Exceptional Handling in test code.  Conclusions: The findings highlight that test smells are often less prioritized for resolution compared to code smells, contributing to their higher survivability. The identified associations offer actionable insights for early detection and refactoring prioritization. These results provide a foundation for improving smell detection tools and practices, ultimately enhancing software quality and maintainability

Contribution Prediction in Federated Learning via Client Behavior Evaluation

Federated learning (FL), a decentralized machine learning framework that allows edge devices (i.e., clients) to train a global model while preserving data/client privacy, has become increasingly popular recently. In FL, a shared global model is built by aggregating the updated parameters in a distributed manner. To incentivize data owners to participate in FL, it is essential for service providers to fairly evaluate the contribution of each data owner to the shared model during the learning process. To the best of our knowledge, most existing solutions are resource-demanding and usually run as an additional evaluation procedure. The latter produces an expensive computational cost for large data owners. In this paper, we present simple and effective FL solutions that show how the clients’ behavior can be evaluated during the training process with respect to reliability, and this is demonstrated for two existing FL models, Cluster Analysis-based Federated Learning (CA-FL) and Group-Personalized FL (GP-FL), respectively. In the former model, CA-FL, the frequency of each client to be selected as a cluster representative and in that way to be involved in the building of the shared model is assessed. This can eventually be considered as a measure of the respective client data reliability. In the latter model, GP-FL, we calculate how many times each client changes a cluster it belongs to during FL training, which can be interpreted as a measure of the client's unstable behavior, i.e., it can be considered as not very reliable. We validate our FL approaches on three LEAF datasets and benchmark their performance to two baseline contribution evaluation approaches. The experimental results demonstrate that by applying the two FL models we are able to get robust evaluations of clients’ behavior during the training process. These evaluations can be used for further studying, comparing, understanding, and eventually predicting clients’ contributions to the shared global model

Göteborg borgar för tillväxt : Vad händer i väst och vad betyder det?

Rapporten Göteborg borgar för tillväxt undersöker om Sveriges ekonomiska centrum håller på att förskjutas västerut till Göteborgsregionen

The growth and development of Nordic regional science research 1982-2022 : bibliometric evidence from thirteen regional science journals

In a paper published in 2020, Philip McCann gives a qualitative overview of "the Nordic contribution to regional science", which, however, only includes contributions by Swedish regional science researchers. The purpose of this paper is to complement McCann's overview by highlighting Nordic contributions to regional science in the period 1983-2022 through a bibliometric analysis of publications in a set of thirteen regional science journals indexed in Web of Science, using several standard bibliometric tools. Our most interesting and surprising result is that the number of "Nordic" publications in the chosen set of journals has grown more than six times faster than the total number of publications in these journals between 1983-1992 and 2013-2022. This implies that the "Nordic" regional science researchers have increased their "market share" from 1.9 to 9.1 per cent. During the same period, the share of co-authored papers increased from 50.0 to 82.9 per cent and the share of international co-authorships increased from 0 to 50.0 per cent and went from being a mainly intra-European activity to a global activity. This process is also reflected in a certain international influence on the research topics during the four periods analysed, but to a considerable extent it seems that the Nordic regional scientists have pursued their own Nordic research themes. In terms of individual research productivity, there was the expected skewed distribution with a small number of researchers with a large research output. At the institutional level, there were notable changes in the ranking of institutions in terms of number of authorships, but one institution-Ume & aring; University-ranked among the Nordic top-2 in all four periods

AI-based fault localization approach for SCADA systems

Background: SCADA (Supervisory Control and Data Acquisition) systems are fundamental to the operation and stability of critical power infrastructure, such as electrical grids. However, fault localization in SCADA systems poses significant challenges due to their heterogeneous nature, comprising tightly integrated hardware and software components, and the sheer volume of data generated during their operation. The interplay between diverse system elements, including sensors, communication protocols, control algorithms, and monitoring software, adds layers of complexity to fault identification and resolution. Traditional fault localization methods—relying on manual log analysis, code reviews, and bug triaging—are often inefficient and struggle to scale with the increasing volume and complexity of SCADA environments. While Artificial Intelligence (AI)-based approaches have demonstrated potential in other domains, their application in the power industry, particularly in SCADA systems, remains underexplored. Objectives: This thesis aims to design, implement, and evaluate an AI fault localization approach tailored for SCADA systems, focusing on improving fault localization and reducing the number of bugs that propagate to production environments. The key innovation lies in guiding pre-trained AI models with domain-specific knowledge derived from SCADA-specific data sources, such as industry-specific bug reports, system logs, and work item histories. Methods: Employing the Design Science Research Process (DSRP), the research begins with problem identification through literature review and expert consultation to understand the limitations of traditional methods and identify opportunities for AI. In the solution design phase, pre-trained AI models are adapted to process SCADA-specific data using techniques such as Retrieval-Augmented Generation (RAG). By integrating historical and operational knowledge, the models are equipped to generate actionable insights tailored to the SCADA domain. The prototype is then empirically evaluated within a SCADA development environment, focusing on metrics such as accuracy, efficiency, and feedback from industry professionals. Results: The Power-RAG prototype, designed for fault localization in SCADA systems, was evaluated across two iterations. In the first iteration, open source PrivateGPT achieved 100% fault localization accuracy but was notably slow, averaging 88 seconds per query. To address this, a custom UI was developed, achieving an impressive 95% accuracy while significantly reducing query time to just 12 seconds—a stark contrast to the 343 seconds required by traditional manual methods. The prototype efficiently provided Area Path suggestions and actionable solution insights, that could lead to improved operational efficiency. Feedback from five industry professionals praised the user-friendliness, adaptability, and speed of the custom UI, while highlighting areas for improvement, including query sensitivity and robustness in handling diverse fault scenarios. These results underscore the balance achieved between speed and accuracy, making the Power-RAG a usable initial prototype for SCADA fault localization. Conclusions: This thesis explores the application of AI-driven fault localization methods within SCADA systems, an area where such implementations remain largely underexplored. By leveraging pre-trained AI models guided with SCADA-specific knowledge, this research demonstrates how these tools can effectively process complex datasets, such as system logs and bug reports, to identify and localize faults. The results show clear improvements in fault detection efficiency, accuracy, and overall system reliability compared to traditional manual approaches.  While the findings highlight the feasibility and potential benefits of AI in enhancing fault localization workflows, it is important to acknowledge that this work represents an initial step rather than a comprehensive solution. The prototype developed in this thesis provides a foundation for further refinement and adaptation.

The illustrative case of the HYBRIT fossil-free steel production initiative in the perspective of industrial symbiosis and convergence

This article attempts to bridge the gap between the concepts of Industrial Symbioses (IS) and Industrial Convergence (IC) by arguing that the two concepts can jointly help to understand the role of industrial structures and value chains that embody transformation processes through which technologies evolve in response to transformation pressure. On one hand, IS with a focus on inter-firm collaborations and resource exchange has become a useful framework to understand and capture the mechanisms that foster sustainable industrial and technological development, while on the other hand IC has been used to analyze technological development that blurs traditional borders between firms in terms of innovations and business development. However, although interrelated the two concepts have been discussed separately. This paper is using the HYBRIT initiative as an illustrative case of a climate change mitigation and as such a “flagship” project in Sweden in an effort to replace the traditional blast furnace technology as the core unit processing technology in steelmaking. It is advocated that whilst many aspects of the conceptual models of IS and IC appear to be congruent with the on-going HYBRIT eco-industrial transformation process, the overall impression is that in future eco-industrial transformations, it could be of interest to develop and deploy a more specific transformation model adapted and capturing unique process-industrial conditions for product and process innovation

Exploring predictors of the five-time sit-to-stand test based on cross-sectional findings from the Swedish National Study on Aging and Care (SNAC)

Background As we age, staying physically active and reducing sedentary behavior becomes crucial. To understand how to achieve this, factors related to daily physical function such as five-time sit-to-stand (STS) time should be explored. This study aimed to investigate the associations between STS time, self-rated physical activity, physical function, health-related quality of life, physical and mental health in community-dwelling older adults aged >= 60 years. Method Cross-sectional design with self-reported and objectively measured data from adults aged >= 60 years (n = 819), acquired from the Swedish National Study on Aging and Care. Data was analyzed through multiple linear regression. Results The model (R-2 = 0.268) showed that STS time was predicted by grip strength (beta' = -0.204, p < 0.05), age (beta' = 0.202, p < 0.05), health-related quality of life (beta' = -0.192, p < 0.05), having fallen within the preceding twelve months (beta' = -0.127, p < 0.05), physical activities of perceived light to moderate intensity (beta' = -0.121, p < 0.05), one-leg stand (beta' = -0.099, p < 0.05), and education level (beta' = -0.092, p < 0.05). For STS time, health-related quality of life (beta = -0.354, confidence interval [CI] (-0.509)-(-0.199)), having fallen within the preceding twelve months (beta = -0.222, CI (-0.365)-(-0.078)), and physical activities of perceived light to moderate intensity (beta = -0.166, CI (-0.278)-(-0.053)) were the most prominent predictors. Conclusion The model highlights the importance of grip strength and health-related quality of life in predicting STS time in older adults. Clinicians can use these insights to develop interventions that maintain physical function by regularly assessing and monitoring these factors. Future research should explore the relationship between fall history, faster STS time, and the impact of grip strength and health-related quality of life on sedentary behavior among older adults.SNA