Examining the Effectiveness of Generative Artificial Intelligence for the Identification of Defeaters in Assurance Cases

Simple item page
dc.contributor.advisorBoaye Belle, Alvine
dc.contributor.authorKhakzad Shahandashti, Kimya
dc.date.accessioned2024-07-18T21:26:43Z
dc.date.available2024-07-18T21:26:43Z
dc.date.copyright2024-04-25
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:26:42Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractAssurance cases are structured arguments that allow verifying the correct implementation of the created systems’ non-functional requirements (e.g., safety, security, reliability). This allows for preventing system failure. The latter may result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Assurance cases support the certification of systems in compliance with industrial standards (e.g., DO-178C, ISO26262). However, the presence of assurance weakeners - deficits and logical fallacies - signals gaps in evidence and reasoning. Addressing this, our research presents a comprehensive taxonomy for categorizing these assurance weakeners, alongside proposed management strategies. The taxonomy divides weakeners into four categories of uncertainty: aleatory, epistemic, ontological, and argumentation. It also categorizes management approaches into representation, identification, and mitigation. A critical aspect of strengthening assurance cases involves identifying argumentation uncertainty or defeaters. To automate this process, we explore the capabilities of GPT-4 Turbo, a sophisticated large language model by OpenAI. We focus on its application in detecting defeaters within assurance cases represented using Eliminative Argumentation(EA) notation. Our initial evaluation assesses GPT-4 Turbo’s proficiency in understanding and applying this notation, a key factor in effectively generating defeaters. The results indicate that GPT-4 Turbo is highly adept in EA notation, demonstrating its potential to generate a diverse range of defeaters, thereby enhancing the robustness and reliability of assurance cases. Moreover, we used GPT-4 Turbo to identify defeaters which demonstrated effective proficiency.
dc.identifier.urihttps://hdl.handle.net/10315/42188
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsAssurance cases
dc.subject.keywordsAssurance deficit
dc.subject.keywordsDefeaters
dc.subject.keywordsLLMs
dc.titleExamining the Effectiveness of Generative Artificial Intelligence for the Identification of Defeaters in Assurance Cases
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
KhakzadShahandashti_Kimya_2024_Masters.pdf
Size:
1.79 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Computer Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.