Belle, Alvine BoayeSivakumar, Mithila2024-07-182024-07-182024-04-032024-07-18https://hdl.handle.net/10315/42158Safety cases play a pivotal role in ensuring system reliability and acceptability, providing a structured argument supported by evidence. However, gaps in safety case literature hinder comprehensive safety assurance practices. In this thesis, we address this challenge through a three-fold approach. First, we conducted a bibliometric analysis following PRISMA 2020 guidelines to identify trends and knowledge gaps in safety assurance research. The analysis reveals critical areas lacking full safety cases and highlights the need for automated safety case construction. Then, we manually constructed a safety case for an ML-enabled component of an autonomous vehicle. Finally, leveraging large language models like GPT-4, we conducted experiments to automate safety case generation. Results indicate that GPT-4 produces safety cases with moderate accuracy and high semantic similarity to ground truth cases. This comprehensive methodology enhances safety practices, aiding researchers, analysts, and regulators in achieving robust safety assurance in complex systems.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceAutomotive engineeringElectronic Thesis or Dissertation2024-07-18Safety caseMachine learningRE for AI-based safety-critical systemsSafety assuranceAutonomous vehicleAssurance casesBibliometric analysisSafety requirementsUncertaintyDomain-specific languageAI and machine learningTesting and assuranceArtificial intelligenceLarge language modelsGenerative AI