ENTERPRISE AI ANALYSIS
Revolutionizing Code Localization with Graph-Guided LLM Agents
Code localization, a critical task in software maintenance, is challenging due to the need for efficient navigation of complex codebases and reasoning across hierarchical structures and dependencies. LOCAGENT addresses this by introducing a framework that leverages graph-based representation and LLM agents for precise, multi-hop reasoning. Our approach significantly enhances accuracy and reduces costs, demonstrating competitive performance with state-of-the-art proprietary models.
Executive Impact & Key Metrics
LOCAGENT revolutionizes code localization for software maintenance by leveraging graph-based representations and LLM agents. Our framework efficiently navigates complex codebases, capturing structural and dependency information (imports, invocations, inheritance). This enables powerful multi-hop reasoning, significantly enhancing localization accuracy on real-world benchmarks. The fine-tuned Qwen-2.5-Coder-Instruct-32B model achieves SOTA-comparable performance with an 86% cost reduction and up to 92.7% file-level accuracy. It also boosts GitHub issue resolution success rates by 12% (Pass@10), streamlining development workflows and addressing critical limitations of existing methods.
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Cost Reduction
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File-level Accuracy (Acc@5)
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Issue Resolution Improvement (Pass@10)
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Indexing Time Per Codebase
Deep Analysis & Enterprise Applications
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Code Localization Fundamentals
Code localization is a fundamental yet challenging task in software maintenance. It involves identifying precise code sections that require modification to resolve issues. Traditional methods often struggle with complex codebases and implicit dependencies, leading to inefficient development cycles and new bugs.
LLM Integration Challenges
Existing approaches, including dense retrieval and agent-based LLMs, face limitations. Dense retrieval requires constant updates for evolving codebases. LLMs, while powerful, are hampered by context window limits and struggle with multi-hop reasoning across complex, implicit code dependencies not directly mentioned in issue descriptions.
Graph-based Representation Advantages
LOCAGENT introduces a novel graph-based indexing that unifies code structures (files, classes, functions) and their dependencies (imports, invocations, inheritance). This structured representation enables LLM agents to perform powerful multi-hop reasoning and navigate complex code relationships efficiently, bridging the gap between natural language issues and specific code elements.
Loc-Bench and Performance Insights
We introduce Loc-Bench, a new benchmark addressing limitations of existing datasets like SWE-Bench by covering diverse maintenance scenarios (bug fixes, features, security, performance) and mitigating data contamination risks. Experimental results show LOCAGENT's significant accuracy gains and cost-efficiency compared to SOTA models.
Enterprise Process Flow: LocAgent Workflow
Code Graph Indexing
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Entity Indexing
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Agent Runtime (Search, Traverse, Retrieve)
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Localized Code Sections
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Cost Reduction in Localization Tasks
| Method | Contain | Import | Inherit | Invoke | Directory | File | Class | Function | Search/Traversal Strategy |
|---|---|---|---|---|---|---|---|---|---|
| CodexGraph | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | Cypher queries |
| RepoGraph | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | Ego-graph retrieval |
| RepoUnderstander | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | MCTS |
| OrcaLoca | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | Simple search tools |
| LOCAGENT (Ours) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Unified retrieval tools |
Superior Performance on SWE-Bench-Lite
LOCAGENT with fine-tuned Qwen-2.5-Coder-Instruct-32B achieves 92.7% accuracy on file-level localization (Acc@5) and 77.01% accuracy on function-level localization (Acc@10). This performance is comparable to state-of-the-art proprietary models like Claude-3.5, while significantly reducing API costs. Our method demonstrates robustness even as task difficulty increases, outperforming traditional retrieval and other agent-based methods.
Calculate Your Potential ROI
Estimate the significant time and cost savings your enterprise could realize by implementing advanced AI-driven code localization.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A typical phased approach to integrating LOCAGENT into your enterprise workflows, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Customization
Initial assessment of your existing codebase and development workflows. Identification of key pain points and tailoring LOCAGENT's graph construction and indexing to your specific repository structures and language requirements.
Phase 2: Integration & Training
Seamless integration of LOCAGENT's API and agent tools into your existing developer environments (IDEs, CI/CD). Comprehensive training for your engineering teams on leveraging the graph-guided LLM agents for efficient code localization.
Phase 3: Pilot Deployment & Optimization
Rollout of LOCAGENT to a pilot team for real-world testing and feedback. Continuous monitoring of localization accuracy, efficiency, and developer satisfaction, followed by iterative optimization of models and tool configurations.
Phase 4: Full-Scale Rollout & Support
Deployment across your entire organization, with ongoing support, performance monitoring, and updates. Establishing internal champions and best practices for sustained productivity gains and reduced maintenance costs.
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