  Code comments can help in program comprehension and are considered as
important artifacts to help developers in software maintenance. However, the
comments are mostly missing or are outdated, specially in complex software
projects. As a result, several automatic comment generation models are
developed as a solution. The recent models explore the integration of external
knowledge resources such as Unified Modeling Language class diagrams to improve
the generated comments. In this paper, we propose API2Com, a model that
leverages the Application Programming Interface Documentations (API Docs) as a
knowledge resource for comment generation. The API Docs include the description
of the methods in more details and therefore, can provide better context in the
generated comments. The API Docs are used along with the code snippets and
Abstract Syntax Trees in our model. We apply the model on a large Java dataset
of over 130,000 methods and evaluate it using both Transformer and RNN-base
architectures. Interestingly, when API Docs are used, the performance increase
is negligible. We therefore run different experiments to reason about the
results. For methods that only contain one API, adding API Docs improves the
results by 4% BLEU score on average (BLEU score is an automatic evaluation
metric used in machine translation). However, as the number of APIs that are
used in a method increases, the performance of the model in generating comments
decreases due to long documentations used in the input. Our results confirm
that the API Docs can be useful in generating better comments, but, new
techniques are required to identify the most informative ones in a method
rather than using all documentations simultaneously.
