Discovering Information Relevant to API Elements Using Text Classification

With the growing size of Application Programming Interfaces (APIs), both API usability and API learning become more challenging. API learning resources are often crucial for helping developers learn an API, but they are distributed across different documents, which makes finding the necessary information more challenging. This work focuses on discovering relevant sections of tutorials for a given API type. We approach this problem by identifying API types in an API tutorial, dividing the tutorial into small fragments and classifying them based on linguistic and structural features. The system we developed can ease information discovery for the developers who need information about a particular API type. Experiments conducted on five tutorials show that our approach is able to discover sections relevant to an API type with 0.79 average precision, 0.73 average recall, and 0.75 average F1 measure when trained and tested on the same tutorial. When trained on four tutorials and tested on a fifth tutorial the average precision is 0.84, average recall is 0.62, and the F1 measure is 0.71.