# ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization arXiv: https://arxiv.org/abs/2605.03117 ยท HTML: https://mlsanity.com/paper/2605.03117/ ## Key facts - Cited by 2 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 24 of its 40 references that are in the map. - First posted on arXiv 4 May 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, 24 of 24) - [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023) - [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) - [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024) - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023) - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020) - [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023) - [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024) - [RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph](https://mlsanity.com/paper/2410.14684/) (2024) - [SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution](https://mlsanity.com/paper/2501.05040/) (2025) - [LocAgent: Graph-Guided LLM Agents for Code Localization](https://mlsanity.com/paper/2503.09089/) (2025) - [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024) - [CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases](https://mlsanity.com/paper/2408.03910/) (2024) - [Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration](https://mlsanity.com/paper/2406.01422/) (2024) - [AgentFL: Scaling LLM-based Fault Localization to Project-Level Context](https://mlsanity.com/paper/2403.16362/) (2024) - [OrcaLoca: An LLM Agent Framework for Software Issue Localization](https://mlsanity.com/paper/2502.00350/) (2025) - [RLCoder: Reinforcement Learning for Repository-Level Code Completion](https://mlsanity.com/paper/2407.19487/) (2024) - [A Deep Dive into Large Language Models for Automated Bug Localization and Repair](https://mlsanity.com/paper/2404.11595/) (2024) - [A Systematic Literature Review on Large Language Models for Automated Program Repair](https://mlsanity.com/paper/2405.01466/) (2024) - [EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair](https://mlsanity.com/paper/2506.10484/) (2025) - [Enhancing repository-level software repair via repository-aware knowledge graphs](https://mlsanity.com/paper/2503.21710/) (2025) - [Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks](https://mlsanity.com/paper/2505.16901/) (2025) - [FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models](https://mlsanity.com/paper/2411.10714/) (2024) - [RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models](https://mlsanity.com/paper/2601.18044/) (2026) ## Built on by (most cited first, 2 of 2) - [EA-Graph: Artifact-Anchored Verification Memory for Coding Agents under Upstream Drift](https://mlsanity.com/paper/2608.04278/) (2026) - [Beyond Textual Repository Exploration: Dual-Modal Structural Reasoning for Agentic Issue Resolution](https://mlsanity.com/paper/2607.01929/) (2026) ## Abstract (arXiv) Automated program repair at repository scale requires an agent to locate a fault among thousands of files and synthesize a correct patch. Existing graph-based agents represent how a repository is organized into files, classes, and functions, but they do not model how variable values flow within a procedure, which leaves the agent without the semantic precision that function-level and line-level localization demand. We present ARISE (Agentic Repository-level Issue Solving Engine), a framework-agnostic toolset that builds a multi-granularity program graph, extending structural relationships down to statement-level nodes connected by intra-procedural definition-use edges, and exposes it through a three-tier tool API that mounts on any tool-use agentic framework. The central primitive is data-flow slicing, a queryable agent tool that traces in a single call which statements define or consume a variable of interest. On SWE-bench Lite (300 real GitHub issues across 11 Python repositories) with the open-source Qwen2.5-Coder-32B-Instruct backbone, mounting ARISE on SWE-agent as the host resolves 22.0% of issues (66/300), a 4.7 percentage-point gain over the unmodified SWE-agent baseline under the identical backbone and host. We show this gain is largely attributable to sharper localization, with Function Recall@1 (R@1) rising from 0.43 to 0.60 (a 40% relative gain) and Line R@1 from 0.26 to 0.41 (a 58% relative gain). Controlled ablations attribute the improvement to the data-flow graph rather than the tool schema, and we further mount the same toolset on a second host framework to study its portability. Decoupled from any single scaffold, the graph builder and slicing API form a drop-in toolset for future repair research.