Currently available thesis topics:
Open Master Thesis Topics for 2024/2025:
THP-2025-1: A field study on the use of ML model requirements, data requirements and data quality requirements in ML-enabled software and models applied in environmental research.
THP-2025-2: A causal approach on identifying the influence of missing data on ML models trained with large environmental datasets.
THP-2025-3: An exploratory study on usage patterns, risks, and implications to critical software design of software used by elderly users in Sweden.
Any of the master thesis topics can also be adopted for a research project in DAT/DIT 640 (https://chalmers.instructure.com/courses/30826).
Open Bachelor Thesis Topics for 2024/2025:
THPB-2025-1: An exploratory study on usage patterns and risks of critical software used by elderly users in Sweden. (Research Topic for 2-3 students)
THPB-2025-2: Designing a control and middleware solution for a highly distributed, synchronised and low-level addressable system of LED light chain arrays. (Engineering Topic for 4-5 students)
Please contact me if you are interested in any of the suggested topics above.
An overview of further available thesis topics is published on our institute’s project website.
Currently running master theses:
All my master students in 2024 have successfully finished their theses.
Previously supervised master theses:
TH-10: Causal Models for Studies on Fault Detection: Mining Software Repositories. A. Levinsson, L. Fransson. 2024
TH-9: An architectural pattern for deep learning code in the automotive industry. B. Razaq, S. Johansson. 2024
TH-8: Requirement representation for safety-critical and fairness aware automotive perception systems: Identifying challenges for cross company collaboration. O. Jokobsson, Z. Rohacova. 2024
TH-7: Data Quality Reporting of Time-Series Data in Open Datasets for Environmental Research. G. Efthymiou. 2024
TH-6: A Software Engineering Perspective on Data Quality Processes in Environmental Research. M. Moen, M. Norén. 2024
TH-5: Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes. M Forsman, Y. Yang. 2024. Full Text
TH-4: Non-functional requirements and their impact on AGV based systems. V Svensson, M Roudsari. 2023. Full Text
TH-3: Challenges in Specifying Safety-Critical Systems with AI-Components. I Malleswaren, S Dinakaran. 2022. Full Text
TH-2: Quality Attributes of Data in Distributed Deep Learning Architectures. SK Pradhan, S Tungal. 2021. Full text
TH-1: Deriving Contextual Definition and Requirements from Use Cases of Autonomous Drive. J Linder, P Subbiah. 2021. Full text