# Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations

arXiv: https://arxiv.org/abs/2106.06110 · HTML: https://mlsanity.com/paper/2106.06110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [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 Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [Automatically Generating Commit Messages from Diffs using Neural Machine Translation](https://mlsanity.com/paper/1708.09492/) (2017)
- [How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset](https://mlsanity.com/paper/1905.13334/) (2019)
- [Learning to Represent Edits](https://mlsanity.com/paper/1810.13337/) (2018)
- [A Survey of Word Embeddings Evaluation Methods](https://mlsanity.com/paper/1801.09536/) (2018)
- [A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes](https://mlsanity.com/paper/1704.04856/) (2017)
- [Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks](https://mlsanity.com/paper/1603.06129/) (2016)
- [Commit2Vec: Learning Distributed Representations of Code Changes](https://mlsanity.com/paper/1911.07605/) (2019)
- [SmartPaste: Learning to Adapt Source Code](https://mlsanity.com/paper/1705.07867/) (2017)
- [TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing](https://mlsanity.com/paper/1910.12306/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)

## Abstract (arXiv)

In recent times, it has been shown that one can use code as data to aid various applications such as automatic commit message generation, automatic generation of pull request descriptions and automatic program repair. Take for instance the problem of commit message generation. Treating source code as a sequence of tokens, state of the art techniques generate commit messages using neural machine translation models. However, they tend to ignore the syntactic structure of programming languages. Previous work, i.e., code2seq has used structural information from Abstract Syntax Tree (AST) to represent source code and they use it to automatically generate method names. In this paper, we elaborate upon this state of the art approach and modify it to represent source code edits. We determine the effect of using such syntactic structure for the problem of classifying code edits. Inspired by the code2seq approach, we evaluate how using structural information from AST, i.e., paths between AST leaf nodes can help with the task of code edit classification on two datasets of fine-grained syntactic edits. Our experiments shows that attempts of adding syntactic structure does not result in any improvements over less sophisticated methods. The results suggest that techniques such as code2seq, while promising, have a long way to go before they can be generically applied to learning code edit representations. We hope that these results will benefit other researchers and inspire them to work further on this problem.