# AutoCodeSherpa: Symbolic Explanations in AI Coding Agents

arXiv: https://arxiv.org/abs/2507.22414 · HTML: https://mlsanity.com/paper/2507.22414/

## 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 20 of its 55 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Placed in

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

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

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents](https://mlsanity.com/paper/2406.12952/) (2024)
- [Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction](https://mlsanity.com/paper/2209.11515/) (2022)
- [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024)
- [Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study](https://mlsanity.com/paper/2503.15223/) (2025)
- [Explainable Automated Debugging via Large Language Model-driven Scientific Debugging](https://mlsanity.com/paper/2304.02195/) (2023)
- [What is it like to program with artificial intelligence?](https://mlsanity.com/paper/2208.06213/) (2022)
- [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025)
- [Otter: Generating Tests from Issues to Validate SWE Patches](https://mlsanity.com/paper/2502.05368/) (2025)
- [Trust Enhancement Issues in Program Repair](https://mlsanity.com/paper/2108.13064/) (2021)
- [Agentic AI Software Engineers: Programming with Trust](https://mlsanity.com/paper/2502.13767/) (2025)
- [Unified Software Engineering Agent as AI Software Engineer](https://mlsanity.com/paper/2506.14683/) (2025)
- [AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions](https://mlsanity.com/paper/2411.18015/) (2024)
- [Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction](https://mlsanity.com/paper/2311.04532/) (2023)

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

- [Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair](https://mlsanity.com/paper/2510.03217/) (2025)

## Abstract (arXiv)

Large language model (LLM) agents integrate external tools with one or more LLMs to accomplish specific tasks. Agents have rapidly been adopted by developers, and they are starting to be deployed in industrial workflows, such as their use to fix static analysis issues from the widely used SonarQube static analyzer. However, the growing importance of agents means their actions carry greater impact and potential risk. Thus, to use them at scale, an additional layer of trust and evidence is necessary. This work presents AutoCodeSherpa, a technique that provides explanations of software issues in the form of symbolic formulae. Inspired by the reachability, infection, and propagation model of software faults, the explanations are composed of input, infection, and output conditions, collectively providing a specification of the issue. In practice, the symbolic explanation is implemented as a combination of a property-based test (PBT) and program-internal symbolic expressions. Critically, this means our symbolic explanations are executable and can be automatically evaluated, unlike natural language explanations. Experiments show the generated conditions are highly accurate. For example, input conditions from AutoCodeSherpa had an accuracy of 85.7%. This high accuracy makes symbolic explanations particularly useful in two scenarios. First, the explanations can be used in automated issue resolution environments to decide whether to accept or reject patches from issue resolution agents; AutoCodeSherpa could reject 2x as many incorrect patches as baselines did. Secondly, as agentic AI approaches continue to develop, program analysis driven explanations like ours can be provided to other LLM-based repair techniques which do not employ analysis to improve their output. In our experiments, our symbolic explanations could improve the plausible patch generation rate of the Agentless technique by 60%.