ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization
arXiv:2605.03117v2 Announce Type: replace-cross Abstract: 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-granular
The rapid advancement in AI models and agentic capabilities is pushing the boundaries of automated software development and repair, making sophisticated solutions like ARISE feasible.
This development significantly enhances the autonomy and capability of AI agents in complex software engineering tasks, potentially impacting developer productivity and software reliability on a large scale.
AI agents will gain increased semantic precision in tackling repository-level code issues, moving beyond file/class organization to variable flow analysis for more accurate fault localization and repair.
- · Software Development Teams
- · AI Agent Developers
- · Cloud Computing Providers
- · Large Enterprises with Extensive Codebases
- · Manual Debugging Tool Vendors
- · Legacy Software Development Methodologies
Reduced time and cost for software bug fixing and maintenance.
Increased software reliability and accelerated development cycles across industries.
A potential shift in programmer roles from debugging to higher-level architectural design and AI supervision.
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Read at arXiv cs.AI