# Project-Level Encoding for Neural Source Code Summarization of Subroutines

arXiv: https://arxiv.org/abs/2103.11599 · HTML: https://mlsanity.com/paper/2103.11599/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (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)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [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)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [Recommendations for Datasets for Source Code Summarization](https://mlsanity.com/paper/1904.02660/) (2019)
- [Automatically Generating Commit Messages from Diffs using Neural Machine Translation](https://mlsanity.com/paper/1708.09492/) (2017)
- [Improved Automatic Summarization of Subroutines via Attention to File Context](https://mlsanity.com/paper/2004.04881/) (2020)
- [A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes](https://mlsanity.com/paper/1704.04856/) (2017)
- [A Multi-Perspective Architecture for Semantic Code Search](https://mlsanity.com/paper/2005.06980/) (2020)
- [Automatic Generation of Text Descriptive Comments for Code Blocks](https://mlsanity.com/paper/1808.06880/) (2018)
- [Action Word Prediction for Neural Source Code Summarization](https://mlsanity.com/paper/2101.02742/) (2021)
- [Context-aware Cascade Attention-based RNN for Video Emotion Recognition](https://mlsanity.com/paper/1805.12098/) (2018)

## Built on by (most cited first, 24 of 24)

- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [Automatic Code Summarization via ChatGPT: How Far Are We?](https://mlsanity.com/paper/2305.12865/) (2023)
- [Distilled GPT for Source Code Summarization](https://mlsanity.com/paper/2308.14731/) (2023)
- [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)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [Model Editing for LLMs4Code: How Far are We?](https://mlsanity.com/paper/2411.06638/) (2024)
- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization](https://mlsanity.com/paper/2305.09773/) (2023)
- [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024)
- [A Language Model of Java Methods with Train/Test Deduplication](https://mlsanity.com/paper/2305.08286/) (2023)
- [Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors](https://mlsanity.com/paper/2106.08415/) (2021)
- [Semantic Similarity Loss for Neural Source Code Summarization](https://mlsanity.com/paper/2308.07429/) (2023)
- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [Revisiting File Context for Source Code Summarization](https://mlsanity.com/paper/2309.02326/) (2023)
- [CoRet: Improved Retriever for Code Editing](https://mlsanity.com/paper/2505.24715/) (2025)
- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation](https://mlsanity.com/paper/2303.01645/) (2023)
- [Statement-based Memory for Neural Source Code Summarization](https://mlsanity.com/paper/2307.11709/) (2023)
- [Reverse Engineering User Stories from Code using Large Language Models](https://mlsanity.com/paper/2509.19587/) (2025)
- [Meta Learning for Code Summarization](https://mlsanity.com/paper/2201.08310/) (2022)
- [CONCORD: Towards a DSL for Configurable Graph Code Representation](https://mlsanity.com/paper/2401.17967/) (2024)
- [AI-Mediated Code Comment Improvement](https://mlsanity.com/paper/2505.09021/) (2025)

## Abstract (arXiv)

Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does without resorting to reading the code itself. Techniques based on neural networks (and encoder-decoder model designs in particular) have established themselves as the state-of-the-art. Yet a problem widely recognized with these models is that they assume the information needed to create a summary is present within the code being summarized itself - an assumption which is at odds with program comprehension literature. Thus a current research frontier lies in the question of encoding source code context into neural models of summarization. In this paper, we present a project-level encoder to improve models of code summarization. By project-level, we mean that we create a vectorized representation of selected code files in a software project, and use that representation to augment the encoder of state-of-the-art neural code summarization techniques. We demonstrate how our encoder improves several existing models, and provide guidelines for maximizing improvement while controlling time and resource costs in model size.