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Code comments are significantly helpful in comprehending software programsand also aid developers to save a great deal of time in software maintenance.Code comment generation aims to automatically predict comments in naturallanguage given a code snippet. Several works investigate the effect ofintegrating external knowledge on the quality of generated comments. In thisstudy, we propose a solution, namely APIContext2Com, to improve theeffectiveness of generated comments by incorporating the pre-definedApplication Programming Interface (API) context. The API context includes thedefinition and description of the pre-defined APIs that are used within thecode snippets. As the detailed API information expresses the functionality of acode snippet, it can be helpful in better generating the code summary. Weintroduce a seq-2-seq encoder-decoder neural network model with different setsof multiple encoders to effectively transform distinct inputs into targetcomments. A ranking mechanism is also developed to exclude non-informativeAPIs, so that we can filter out unrelated APIs. We evaluate our approach usingthe Java dataset from CodeSearchNet. The findings reveal that the proposedmodel 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 studiesconfirm the quality of the generated comments and the effect of architectureand ranking APIs.

APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation