Empirical Analysis and Enhancement of Machine Learning Software Documentation

As 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.