ARISE: A Repository-level Graph Representation and Toolset for Agentic Fault Localization and Program Repair
Shahd Seddik, Fatemeh Fard

TL;DR
ARISE introduces a multi-granularity program graph with data-flow analysis to enhance fault localization and program repair using large language models, significantly improving localization and repair success rates.
Contribution
It presents ARISE, a novel graph-based framework that models variable data flow at statement level, enabling more precise fault localization and repair with LLMs.
Findings
ARISE improves Function Recall@1 by 17 points.
ARISE increases Line Recall@1 by 15 points.
ARISE achieves 22% Pass@1 repair success, a 4.7-point improvement.
Abstract
Repository-level fault localization (FL) and automated program repair (APR) require an agent to identify the relevant code units across files, follow call and data dependencies, and generate a valid patch. Existing graph-based systems provide structural representations of repositories (files, classes, functions and their relationships) but do not model how variable values flow within procedures, leaving agents without the semantic precision needed for function- and line-level localization. We present ARISE (Agentic Repository-level Issue Solving Engine), which augments an LLM-based agent with a multi-granularity program graph that extends structural relationships down to statement-level nodes connected by intra-procedural definition-use edges. ARISE exposes this graph through a three-tier tool API, which brings data-flow slicing as a first-class, queryable agent primitive that allows…
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