OntoCat: Automatically categorizing knowledge in API Documentation
This work addresses the challenge for developers in efficiently accessing knowledge from large and varied API documentation, though it is incremental as it builds on an existing taxonomy.
The paper tackles the problem of navigating complex API documentation by introducing OntoCat, a domain-independent technique that automatically categorizes knowledge types in API reference documentation using nine features and their combinations, achieving effective results on Python API documentation.
Most application development happens in the context of complex APIs; reference documentation for APIs has grown tremendously in variety, complexity, and volume, and can be difficult to navigate. There is a growing need to develop well-organized ways to access the knowledge latent in the documentation; several research efforts deal with the organization (ontology) of API-related knowledge. Extensive knowledge-engineering work, supported by a rigorous qualitative analysis, by Maalej & Robillard [3] has identified a useful taxonomy of API knowledge. Based on this taxonomy, we introduce a domain independent technique to extract the knowledge types from the given API reference documentation. Our system, OntoCat, introduces total nine different features and their semantic and statistical combinations to classify the different knowledge types. We tested OntoCat on python API reference documentation. Our experimental results show the effectiveness of the system and opens the scope of probably related research areas (i.e., user behavior, documentation quality, etc.).