Automatic recognizing relevant fragments of APIs using API references

Di Wu, Yang Feng, Hongyu Zhang, Baowen Xu

Abstract: API tutorials are crucial resources as they often provide detailed explanations of how to utilize APIs. Typically, an API tutorial is segmented into a number of consecutive fragments.. If a fragment explains API usage, we regard it as a relevant fragment of the API. Recognizing relevant fragments can aid developers in comprehending, learning, and using APIs. Recently, some studies have presented relevant fragments recognition approaches, which mainly focused on using API tutorials or Stack Overflow to train the recognition model. API references are also important API learning resources as they contain abundant API knowledge. Considering the similarity between API tutorials and API references (both provide API knowledge), we believe that using API knowledge from API references could help recognize relevant tutorial fragments of APIs effectively. However, it is non-trivial to leverage API references to build a supervised learning-based recognition model. Two major problems are the lack of labeled API references and the unavailability of engineered features of API references. We propose a supervised learning based approach named RRTR (which stands for Recognize Relevant Tutorial fragments using API