Enhancing API Documentation through BERTopic Modeling and Summarization
AmirHossein Naghshzan Sylvie Ratte
As the amount of textual data in various fields, including software
development, continues to grow, there is a pressing demand for efficient and
effective extraction and presentation of meaningful insights. This paper
presents a unique approach to address this need, focusing on the complexities
of interpreting Application Programming Interface (API) documentation. While
official API documentation serves as a primary source of information for
developers, it can often be extensive and lacks user-friendliness. In light of
this, developers frequently resort to unofficial sources like Stack Overflow
and GitHub. Our novel approach employs the strengths of BERTopic for topic
modeling and Natural Language Processing (NLP) to automatically generate
summaries of API documentation, thereby creating a more efficient method for
developers to extract the information they need. The produced summaries and
topics are evaluated based on their performance, coherence, and
interoperability.
  The findings of this research contribute to the field of API documentation
analysis by providing insights into recurring topics, identifying common
issues, and generating potential solutions. By improving the accessibility and
efficiency of API documentation comprehension, our work aims to enhance the
software development process and empower developers with practical tools for
navigating complex APIs.