# API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations arXiv: https://arxiv.org/abs/2103.10668 ยท HTML: https://mlsanity.com/paper/2103.10668/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 48 references that are in the map. - First posted on arXiv 19 Mar 2021. ## Builds on (most cited first, 24 of 24) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018) - [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018) - [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019) - [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019) - [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017) - [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018) - [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020) - [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018) - [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016) - [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020) - [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019) - [Code Generation as a Dual Task of Code Summarization](https://mlsanity.com/paper/1910.05923/) (2019) - [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/1910.10419/) (2019) - [Improved Automatic Summarization of Subroutines via Attention to File Context](https://mlsanity.com/paper/2004.04881/) (2020) - [Automatic Source Code Summarization with Extended Tree-LSTM](https://mlsanity.com/paper/1906.08094/) (2019) - [Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data](https://mlsanity.com/paper/1908.00449/) (2019) - [Automatic Code Summarization: A Systematic Literature Review](https://mlsanity.com/paper/1909.04352/) (2019) - [TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search](https://mlsanity.com/paper/2003.03238/) (2020) ## Built on by (most cited first, 10 of 10) - [Automatic Code Summarization via ChatGPT: How Far Are We?](https://mlsanity.com/paper/2305.12865/) (2023) - [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023) - [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022) - [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025) - [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024) - [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023) - [APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation](https://mlsanity.com/paper/2303.01645/) (2023) - [FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis](https://mlsanity.com/paper/2111.07238/) (2021) - [LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition](https://mlsanity.com/paper/2204.09654/) (2022) - [Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations](https://mlsanity.com/paper/2602.03400/) (2026) ## Abstract (arXiv) 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.