# Understanding Programs by Exploiting (Fuzzing) Test Cases

arXiv: https://arxiv.org/abs/2305.13592 · HTML: https://mlsanity.com/paper/2305.13592/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [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)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation](https://mlsanity.com/paper/2108.04556/) (2021)
- [StructCoder: Structure-Aware Transformer for Code Generation](https://mlsanity.com/paper/2206.05239/) (2022)
- [How could Neural Networks understand Programs?](https://mlsanity.com/paper/2105.04297/) (2021)
- [ProGraML: Graph-based Deep Learning for Program Optimization and Analysis](https://mlsanity.com/paper/2003.10536/) (2020)
- [ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning](https://mlsanity.com/paper/2301.09072/) (2023)
- [Angora: Efficient Fuzzing by Principled Search](https://mlsanity.com/paper/1803.01307/) (2018)

## Built on by (most cited first, 13 of 13)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [The Program Testing Ability of Large Language Models for Code](https://mlsanity.com/paper/2310.05727/) (2023)
- [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)
- [Research and application of artificial intelligence based webshell detection model: A literature review](https://mlsanity.com/paper/2405.00066/) (2024)
- [FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation](https://mlsanity.com/paper/2406.08665/) (2024)
- [Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples](https://mlsanity.com/paper/2402.07408/) (2024)
- [Content Fuzzing for Escaping Information Cocoons on Digital Social Media](https://mlsanity.com/paper/2604.05461/) (2026)
- [Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead](https://mlsanity.com/paper/2503.00795/) (2025)
- [Invariant Pretraining for Robust Code Representations](https://mlsanity.com/paper/2608.15412/) (2026)
- [Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills](https://mlsanity.com/paper/2609.12012/) (2026)

## Abstract (arXiv)

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentially different from texts after all, in a sense that they are normally heavily structured and syntax-strict. In particular, programs and their basic units (i.e., functions and subroutines) are designed to demonstrate a variety of behaviors and/or provide possible outputs, given different inputs. The relationship between inputs and possible outputs/behaviors represents the functions/subroutines and profiles the program as a whole. Therefore, we propose to incorporate such a relationship into learning, for achieving a deeper semantic understanding of programs. To obtain inputs that are representative enough to trigger the execution of most part of the code, we resort to fuzz testing and propose fuzz tuning to boost the performance of program understanding and code representation learning, given a pre-trained LLM. The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins. Code is available at https://github.com/rabbitjy/FuzzTuning.