# FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation

arXiv: https://arxiv.org/abs/2406.08665 · HTML: https://mlsanity.com/paper/2406.08665/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [SantaCoder: don't reach for the stars!](https://mlsanity.com/paper/2301.03988/) (2023)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [TOGA: A Neural Method for Test Oracle Generation](https://mlsanity.com/paper/2109.09262/) (2021)
- [On Learning Meaningful Assert Statements for Unit Test Cases](https://mlsanity.com/paper/2002.05800/) (2020)
- [ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation](https://mlsanity.com/paper/2307.00588/) (2023)
- [Learning Deep Semantics for Test Completion](https://mlsanity.com/paper/2302.10166/) (2023)
- [CAT-LM: Training Language Models on Aligned Code And Tests](https://mlsanity.com/paper/2310.01602/) (2023)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [NEUZZ: Efficient Fuzzing with Neural Program Smoothing](https://mlsanity.com/paper/1807.05620/) (2018)
- [Angora: Efficient Fuzzing by Principled Search](https://mlsanity.com/paper/1803.01307/) (2018)
- [LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing](https://mlsanity.com/paper/2406.07714/) (2024)
- [Understanding Programs by Exploiting (Fuzzing) Test Cases](https://mlsanity.com/paper/2305.13592/) (2023)
- [BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes](https://mlsanity.com/paper/1903.06725/) (2019)
- [Code Representation Pre-training with Complements from Program Executions](https://mlsanity.com/paper/2309.09980/) (2023)
- [UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing](https://mlsanity.com/paper/2402.03396/) (2024)
- [IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation](https://mlsanity.com/paper/2402.05256/) (2024)

## Built on by (most cited first, 3 of 3)

- [Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis](https://mlsanity.com/paper/2510.26423/) (2025)
- [Content Fuzzing for Escaping Information Cocoons on Digital Social Media](https://mlsanity.com/paper/2604.05461/) (2026)
- [Invariant Pretraining for Robust Code Representations](https://mlsanity.com/paper/2608.15412/) (2026)

## Abstract (arXiv)

Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particularly methods utilizing large language models, has become increasingly popular. These neural approaches generate semantically meaningful tests that are more maintainable compared with traditional automatic testing methods like fuzzing. However, the diversity and volume of unit tests in current datasets are limited, especially for newer but important languages. In this paper, we present a novel data augmentation technique, FuzzAug, that introduces the benefits of fuzzing to large language models by introducing valid testing semantics and providing diverse coverage-guided inputs. Doubling the size of training datasets, FuzzAug improves the performance from the baselines significantly. This technique demonstrates the potential of introducing prior knowledge from dynamic software analysis to improve neural test generation, offering significant enhancements in neural test generation.