# SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics

arXiv: https://arxiv.org/abs/2104.14671 · HTML: https://mlsanity.com/paper/2104.14671/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [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)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [Graph-based, Self-Supervised Program Repair from Diagnostic Feedback](https://mlsanity.com/paper/2005.10636/) (2020)
- [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [DeepBugs: A Learning Approach to Name-based Bug Detection](https://mlsanity.com/paper/1805.11683/) (2018)
- [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](https://mlsanity.com/paper/2003.07914/) (2020)
- [On Learning Meaningful Code Changes via Neural Machine Translation](https://mlsanity.com/paper/1901.09102/) (2019)
- [ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation](https://mlsanity.com/paper/2104.08006/) (2021)
- [Patching as Translation: the Data and the Metaphor](https://mlsanity.com/paper/2008.10707/) (2020)
- [Learning to Find Usages of Library Functions in Optimized Binaries](https://mlsanity.com/paper/2103.05221/) (2021)
- [Deep Reinforcement Learning for Programming Language Correction](https://mlsanity.com/paper/1801.10467/) (2018)
- [Learning Lenient Parsing & Typing via Indirect Supervision](https://mlsanity.com/paper/1910.05879/) (2019)

## Built on by (most cited first, 8 of 8)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction](https://mlsanity.com/paper/2406.00755/) (2024)
- [LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems](https://mlsanity.com/paper/2401.05443/) (2024)
- [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023)
- [Automated Program Repair: Emerging trends pose and expose problems for benchmarks](https://mlsanity.com/paper/2405.05455/) (2024)
- [ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding](https://mlsanity.com/paper/2504.00019/) (2025)
- [Collaborative Agents for Automated Program Repair in Ruby](https://mlsanity.com/paper/2511.03925/) (2025)
- [On ML-Based Program Translation: Perils and Promises](https://mlsanity.com/paper/2302.10812/) (2023)

## Abstract (arXiv)

Beginning programmers struggle with the complex grammar of modern programming languages like Java, and make lot of syntax errors. The diagnostic syntax error messages from compilers and IDEs are sometimes useful, but often the messages are cryptic and puzzling. Students could be helped, and instructors' time saved, by automated repair suggestions when dealing with syntax errors. Large samples of student errors and fixes are now available, offering the possibility of data-driven machine-learning approaches to help students fix syntax errors. Current machine-learning approaches do a reasonable job fixing syntax errors in shorter programs, but don't work as well even for moderately longer programs. We introduce SYNFIX, a machine-learning based tool that substantially improves on the state-of-the-art, by learning to use compiler diagnostics, employing a very large neural model that leverages unsupervised pre-training, and relying on multi-label classification rather than autoregressive synthesis to generate the (repaired) output. We describe SYNFIX's architecture in detail, and provide a detailed evaluation. We have built SYNFIX into a free, open-source version of Visual Studio Code; we make all our source code and models freely available.