# A Neural Model for Generating Natural Language Summaries of Program Subroutines

arXiv: https://arxiv.org/abs/1902.01954 · HTML: https://mlsanity.com/paper/1902.01954/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 30 of 77)

- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
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- [TreeBERT: A Tree-Based Pre-Trained Model for Programming Language](https://mlsanity.com/paper/2105.12485/) (2021)
- [Automatic Code Summarization via ChatGPT: How Far Are We?](https://mlsanity.com/paper/2305.12865/) (2023)
- [Learning Deep Semantics for Test Completion](https://mlsanity.com/paper/2302.10166/) (2023)
- [Distilled GPT for Source Code Summarization](https://mlsanity.com/paper/2308.14731/) (2023)
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- [CAT-LM: Training Language Models on Aligned Code And Tests](https://mlsanity.com/paper/2310.01602/) (2023)
- [Project-Level Encoding for Neural Source Code Summarization of Subroutines](https://mlsanity.com/paper/2103.11599/) (2021)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code](https://mlsanity.com/paper/2205.11739/) (2022)
- [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://mlsanity.com/paper/2305.06156/) (2023)
- [Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting](https://mlsanity.com/paper/2103.07845/) (2021)
- [Analyzing the Performance of Large Language Models on Code Summarization](https://mlsanity.com/paper/2404.08018/) (2024)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [EditSum: A Retrieve-and-Edit Framework for Source Code Summarization](https://mlsanity.com/paper/2308.13775/) (2023)
- [Assemble Foundation Models for Automatic Code Summarization](https://mlsanity.com/paper/2201.05222/) (2022)
- [Source Code Summarization with Structural Relative Position Guided Transformer](https://mlsanity.com/paper/2202.06521/) (2022)
- [M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization](https://mlsanity.com/paper/2203.09707/) (2022)
- [An Exploratory Study on Code Attention in BERT](https://mlsanity.com/paper/2204.10200/) (2022)
- [Impact of Evaluation Methodologies on Code Summarization](https://mlsanity.com/paper/2108.09619/) (2021)
- [AutoPruner: Transformer-Based Call Graph Pruning](https://mlsanity.com/paper/2209.03230/) (2022)
- [Studying LLM Performance on Closed- and Open-source Data](https://mlsanity.com/paper/2402.15100/) (2024)
- [API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations](https://mlsanity.com/paper/2103.10668/) (2021)

## Abstract (arXiv)

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.