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- PostCausal Models Applied to Studies within the Mining Software Repository Domain(2024) LEVINSSON, AMANDA; FRANSSON, LINNÉA; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Feldt, Robert; Torkar, RichardContext: Research conducted in the mining software repository domain commonly utilize observational data, due to software repositories serving as a rich source of such data. Simultaneously, there is a clear lack regarding the incorporation of causality in Software Engineering (SE) research, whilst statistical analyses often are conducted. Objective: To analyse the practical implications of applying causal models to studies from the Mining Software Repository (MSR) conference. Specifically, it is of interest to examine whether researchers accidentally have included variables (colliders) in their analyses which have biased their results. Method: A computer simulation was utilized as research methodology. This included the steps of (1) identifying a paper with colliders by sampling from the MSR conference and constructing Directed Acylic Graphs (DAGs), (2) a theoretical computer simulation of an SE scenario to prove collider effects, (3) computer simulations utilizing generated synthetic data based on the identified research paper. In addition, an analysis was conducted using the original data from chosen paper. Results: A lack of transparency amongst the research investigated was identified, where variable selection processes and underlying assumptions were not completely clear. Three papers were investigated in the first step of constructing DAGs. Subsequently, colliders were identified in the paper of Nagy and Abdalkareem [46]. Simulations revealed that the exclusion of collider variables improved the sought after effect sizes. However, no practical implications were possible to determine. Replication package available 1. Conclusion: A lack of transparency hindered the construction of DAGs, and indicated a threat to advancements in research. This, due to the need of interpreting authors’ assumptions in their research. An incorporation of causality and DAGs could, due to the increased transparency it would bring, in the long run result in more robust advancements in research. Additionally, DAGs are recommended as tools to mitigate the risk of accidentally conditioning on colliders.
- PostProgress-Based Distributed Queues - Exploring the effects of a novel heuristic with partial queues using fetch-and-add(2024) Hermansson, Sebastian; Johansson, Elias; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Seger, Carl-Johan; Tsigas, PhilippasWith the increasing use of multi-threaded processors comes the challenge of scaling with a higher number of threads. To that end, the semantics of a data structure can be relaxed to lessen contention and achieve better performance. The data structure of interest in this thesis is a relaxed first-in-first-out queue called d-RA, which is made up of partial queues. It utilizes a length-based heuristic, that chooses a queue to perform an operation on. However, it employs relatively slow lock-free partial queues and uses a heuristic that is not definitively proven to be optimal. Furthermore, relaxed first-in-first-out queues lack real-world applications as they can not be used if the order of operations is strict. This thesis improves on the d-RA algorithm by using faster partial queues and a new progress-based heuristic, which generally increases the data structure’s throughput, causes it to scale with more threads, and lowers the level of relaxation. Additionally, we use relaxed queues in an unordered breadth-first-search to calculate the shortest paths in graphs where they are shown to outperform concurrent queues.
- PostReinforcement Learning-Based Cell Balancing for Electric Vehicles(2024) MAZZOLO, GIOVANNI; SCHIOPU, MATEI; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Petersen Moura Trancoso, Pedro; Petersen Moura Trancoso, PedroLithium-ion battery packs are comprised of hundreds to thousands of individual cells which, even though manufactured uniformly, exhibit small variations in their characteristics that impact their behavior during operation. These differences cause cells’ State of Charge (SOC) to become unbalanced, which can, in turn, reduce the capacity utilization efficiency of the pack [1]. Additionally, battery cells age differently over time, and fast-aged cells can cause packs with healthy cells to be retired early, without fully taking advantage of each cell. When a battery has deteriorated to around 80% of its total capacity, it is retired from electric vehicle usage [2]. To maintain batteries functioning correctly, cell SOC balancing must be done on battery packs. However, balancing the SOC of cells provides a window of opportunity to also include cells’ health into the balancing equation, aiming for the homogenization of cell aging, allowing to thoroughly utilize a battery’s resources. In this way, it is possible to both keep batteries in operating condition and potentially increase their lifespan. In this work, we develop and research a multi-cell simulation framework and Reinforcement Learning (RL) methodologies to explore the potential of cell SOC and health balancing. We propose an active balancing strategy for re-configurable cell topology with RL, in which instead of transferring energy between high SOC cells to low SOC cells, cell utilization is modulated so that the power consumption is optimally distributed based on each cell’s SOC. This strategy is applied to SOC balancing, as well as SOC and State of Health (SOH) balancing simultaneously, to potentially allow for an exhaustive utilization of the battery’s potential.
- PostApplicability of offshore software development best practices to AI-assisted software development(2024) Ljung, Ebba; Ljung, Oliver; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Fotrousi, Farnaz; Ronanki, KrishnaIn this study we explored the similarities between offshore software development and Artificial Intelligence (AI)-assisted software development. Given the current research gap regarding best practices for AI-assisted software development, this thesis aimed to explore the applicability of offshore software development best practices to AI-assisted software development. By gathering challenges within both areas through a literature review and using framework analysis, we were able to determine similarities between the two areas. With structured interviews we were then able to use the identified similarities to further examine the challenges and determine the applicability of offshore software development best practices to AI-assisted software development. The findings revealed three shared challenges: IP theft, code privacy, and tool incompatibility. Additionally, one best practice, confidentiality agreements, was determined to be directly applicable to AI-assisted software development based on the responses of participants who regularly use AI assistants for coding. The insights gained from this thesis provide valuable guidance for industry practitioners and contribute to further academia on optimising AI-assisted software development practices.
- PostFAUXperience Framework - Designing For Critical Conscious AI Use In Higher Education(2024) DE SOUSA NUNES, JOSÉ BENER; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Björk, Staffan; Ljungblad, Sararequires the development of clear principles for its ethical and responsible use. Despite numerous potential benefits, such as personalized learning and task optimization, this technology poses ethical concerns regarding biases, misinformation, and the difficulty of differentiating human-created texts from AI-generated content. To address these concerns, we created the "FAUXperience Framework" to offer guidelines for the ethical use of AI in education by fostering a critical consciousness about this technology’s potential benefits and risks. The framework results from user research with stakeholders such as teachers, students, and other interested parties. The study focused on collecting qualitative and quantitative data on how stakeholders experience using generative AI tools in their academic activities and how universities handle the issues related to the misuse of AI. The term "FAUXperience" combines "faux," which refers to artificiality, and "experience," to indicate the use of AI to artificially enhance the educational process in addition to traditional teaching methods. By fostering critical consciousness about AI, the framework aims to promote the benefits of learning associated with AI while drawing stakeholders’ attention to its impact on education. In conclusion, the "FAUXperience Framework" encourages teachers and students to be critically conscious actors in AI-powered education.