  Code comments are significantly helpful in comprehending software programs
and also aid developers to save a great deal of time in software maintenance.
Code comment generation aims to automatically predict comments in natural
language given a code snippet. Several works investigate the effect of
integrating external knowledge on the quality of generated comments. In this
study, we propose a solution, namely APIContext2Com, to improve the
effectiveness of generated comments by incorporating the pre-defined
Application Programming Interface (API) context. The API context includes the
definition and description of the pre-defined APIs that are used within the
code snippets. As the detailed API information expresses the functionality of a
code snippet, it can be helpful in better generating the code summary. We
introduce a seq-2-seq encoder-decoder neural network model with different sets
of multiple encoders to effectively transform distinct inputs into target
comments. A ranking mechanism is also developed to exclude non-informative
APIs, so that we can filter out unrelated APIs. We evaluate our approach using
the Java dataset from CodeSearchNet. The findings reveal that the proposed
model improves the best baseline by 1.88 (8.24 %), 2.16 (17.58 %), 1.38 (18.3
%), 0.73 (14.17 %), 1.58 (14.98 %) and 1.9 (6.92 %) for BLEU1, BLEU2, BLEU3,
BLEU4, METEOR, ROUGE-L respectively. Human evaluation and ablation studies
confirm the quality of the generated comments and the effect of architecture
and ranking APIs.
