# AgentExecutor: Partial Code Execution via Agentic Context Generation

arXiv: https://arxiv.org/abs/2608.05959 · HTML: https://mlsanity.com/paper/2608.05959/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 15 of 15)

- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Reasoning Runtime Behavior of a Program with LLM: How Far Are We?](https://mlsanity.com/paper/2403.16437/) (2024)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation](https://mlsanity.com/paper/2307.12469/) (2023)
- [Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python](https://mlsanity.com/paper/2101.04470/) (2021)
- [Automated Unit Test Generation for Python](https://mlsanity.com/paper/2007.14049/) (2020)
- [Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks](https://mlsanity.com/paper/2505.08903/) (2025)
- [ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space](https://mlsanity.com/paper/2506.10323/) (2025)
- [LExecutor: Learning-Guided Execution](https://mlsanity.com/paper/2302.02343/) (2023)
- [SelfPiCo: Self-Guided Partial Code Execution with LLMs](https://mlsanity.com/paper/2407.16974/) (2024)
- [ChangeGuard: Validating Code Changes via Pairwise Learning-Guided Execution](https://mlsanity.com/paper/2410.16092/) (2024)
- [Katana: Dual Slicing-Based Context for Learning Bug Fixes](https://mlsanity.com/paper/2205.00180/) (2022)
- [Treefix: Enabling Execution with a Tree of Prefixes](https://mlsanity.com/paper/2501.12339/) (2025)
- [LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis](https://mlsanity.com/paper/2601.05539/) (2026)

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

- [Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?](https://mlsanity.com/paper/2610.04716/) (2026)

## Abstract (arXiv)

Executing code snippets is essential for dynamic program analysis, but it remains challenging to execute an arbitrary code snippet due to issues like missing context and incomplete dependencies. Existing approaches to partial code execution, such as LExecutor and Treefix, leverage the power of language models to infer missing information and enable execution. However, they suffer from (i) limited action spaces and feedback, and (ii) rigid optimization strategies, which restrict their effectiveness and efficiency. In this paper, we propose AgentExecutor, a novel multi-agent framework for partial code execution. Our approach introduces a three-phase design: execution environment preparation, dynamic exploration with iterative refinement, and prefix evolution via program synthesis. Supported by the power of LLM agents who can think, act, and get feedback iteratively, AgentExecutor is able to autonomously explore a richer action space, enabling diverse operations such as creating resource files and resolving environment configuration. Furthermore, it adopts adaptive optimization strategies, including coverage-guided context pruning and prefix evolution via program synthesis, to systematically improve the execution quality of partial code. We evaluate AgentExecutor on two widely used datasets comprising Stack Overflow snippets and open-source project code. The results show that AgentExecutor achieves up to 94% and 90% code coverage, outperforming the state-of-the-art approach Treefix by 19.9% and 13.8%, respectively. In addition, AgentExecutor significantly reduces execution time (by up to 80.3%) and cost (by up to 56.6%). These findings demonstrate that AgentExecutor provides an effective and efficient solution for partial code execution.