# A Contemporary Survey of Large Language Model Assisted Program Analysis

arXiv: https://arxiv.org/abs/2502.18474 · HTML: https://mlsanity.com/paper/2502.18474/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 87 of its 177 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 30 of 87)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://mlsanity.com/paper/2405.17238/) (2024)
- [ChatUniTest: A Framework for LLM-Based Test Generation](https://mlsanity.com/paper/2305.04764/) (2023)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2404.13161/) (2024)
- [Fuzz4All: Universal Fuzzing with Large Language Models](https://mlsanity.com/paper/2308.04748/) (2023)
- [No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation](https://mlsanity.com/paper/2305.04207/) (2023)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks](https://mlsanity.com/paper/1803.01937/) (2018)
- [DeepBugs: A Learning Approach to Name-based Bug Detection](https://mlsanity.com/paper/1805.11683/) (2018)
- [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023)
- [SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2407.00952/) (2024)
- [Enchanting Program Specification Synthesis by Large Language Models using Static Analysis and Program Verification](https://mlsanity.com/paper/2404.00762/) (2024)
- [Prompt-Enhanced Software Vulnerability Detection Using ChatGPT](https://mlsanity.com/paper/2308.12697/) (2023)
- [Pynguin: Automated Unit Test Generation for Python](https://mlsanity.com/paper/2202.05218/) (2022)
- [Lemur: Integrating Large Language Models in Automated Program Verification](https://mlsanity.com/paper/2310.04870/) (2023)

## Built on by (most cited first, 11 of 11)

- [State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning](https://mlsanity.com/paper/2503.20613/) (2025)
- [Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks](https://mlsanity.com/paper/2503.20844/) (2025)
- [Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents](https://mlsanity.com/paper/2604.27283/) (2026)
- [SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework](https://mlsanity.com/paper/2504.13479/) (2025)
- [Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection](https://mlsanity.com/paper/2507.22772/) (2025)
- [A Decompilation-Driven Framework for Malware Detection with Large Language Models](https://mlsanity.com/paper/2601.09035/) (2026)
- [Satisfiability Solving with LLMs: A Matched-Pair Evaluation of Reasoning Capability](https://mlsanity.com/paper/2605.28602/) (2026)
- [Automated detection of atomicity violations in large-scale systems](https://mlsanity.com/paper/2504.00521/) (2025)
- [UIXPOSE: Mobile Malware Detection via Intention-Behaviour Discrepancy Analysis](https://mlsanity.com/paper/2512.14130/) (2025)
- [Detecting Vulnerability-Inducing Commits via Multi-Stage Reasoning with LLM-Based Agents](https://mlsanity.com/paper/2607.05772/) (2026)
- [Hiding in Plain Sight: An Effective Physical Adversarial Patch Attack against Visual-Infrared Fused Face Detection](https://mlsanity.com/paper/2607.23292/) (2026)

## Abstract (arXiv)

The increasing complexity of software systems has driven significant advancements in program analysis, as traditional methods unable to meet the demands of modern software development. To address these limitations, deep learning techniques, particularly Large Language Models (LLMs), have gained attention due to their context-aware capabilities in code comprehension. Recognizing the potential of LLMs, researchers have extensively explored their application in program analysis since their introduction. Despite existing surveys on LLM applications in cybersecurity, comprehensive reviews specifically addressing their role in program analysis remain scarce. In this survey, we systematically review the application of LLMs in program analysis, categorizing the existing work into static analysis, dynamic analysis, and hybrid approaches. Moreover, by examining and synthesizing recent studies, we identify future directions and challenges in the field. This survey aims to demonstrate the potential of LLMs in advancing program analysis practices and offer actionable insights for security researchers seeking to enhance detection frameworks or develop domain-specific models.