Empirical Analysis and Enhancement of Machine Learning Software Documentation

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dc.contributor.advisorNayebi, Maleknaz
dc.contributor.advisorDatta, Suprakash
dc.contributor.authorSharuka Promodya Thirimanne
dc.date.accessioned2024-11-07T11:20:29Z
dc.date.available2024-11-07T11:20:29Z
dc.date.copyright2024-10-01
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:20:28Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractAs machine learning gains popularity, individuals from diverse fields and skill levels integrate it into their workflows. However, many lack software engineering experience, impacting the usability of documentation. Additionally, the current machine learning documentation and its issues are insufficiently addressed in the literature. This thesis comprises two papers. In the first paper, we compared the content and design differences of TensorFlow tutorials and analyzed the profiles of users who asked questions about them. We also developed a comprehensive taxonomy of TensorFlow documentation issues. In the second paper, we examined the potential of leveraging generative AI to augment machine learning documentation. We proposed a method to augment TensorFlow API documentation by addressing documentation-related questions using large language models. This thesis highlights the need for machine learning documentation to accommodate diverse skill levels as its use expands across domains and showcases the potential of generative AI to automate documentation augmentation.
dc.identifier.urihttps://hdl.handle.net/10315/42522
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsSoftware engineering
dc.subject.keywordsMachine learning
dc.subject.keywordsDocumentation
dc.subject.keywordsCase study
dc.subject.keywordsTensorFlow
dc.subject.keywordsLarge language model
dc.subject.keywordsLLM
dc.subject.keywordsRetrieval augmented generation (RAG)
dc.titleEmpirical Analysis and Enhancement of Machine Learning Software Documentation
dc.typeElectronic Thesis or Dissertation

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