Enterprise AI Analysis
Automated Deep Learning Bug Reproduction with Intelligent Agents
Our cutting-edge platform, RepGen, leverages advanced AI to systematically identify, reproduce, and analyze deep learning bugs, significantly enhancing developer productivity and system reliability. Explore how we achieve an 80.19% reproduction rate, reducing manual effort by 56.8%.
Revolutionizing DL System Reliability
The RepGen platform delivers tangible benefits across key operational metrics, transforming how enterprises approach deep learning bug resolution.
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Bug Reproduction Rate
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Time Saved per Bug
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Success Rate Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
RepGen's Automated Workflow
RepGen employs a novel agentic workflow that constructs a learning-enhanced context, develops a targeted reproduction plan, and uses a feedback-driven agent for high-quality code generation capable of reproducing DL bugs efficiently.
Enterprise Process Flow
Construction of Learning-Enhanced Context
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Bug Report Restructuring
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Generation of Plans
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Reproduction Agent
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Reproducible Code Snippet
RepGen vs. Baselines: Success Rate
RepGen achieved an 80.19% success rate in bug reproduction, surpassing all LLM-only baselines. This represents a significant improvement in the reliability and efficiency of deep learning bug resolution.
| Model | Success Rate (%) |
|---|---|
| RepGen | 80.19 |
| DeepSeek-R1-685B (Few-Shot) | 60.38 |
| GPT-4.1 (Few-Shot) | 53.77 |
| Llama-3-70B (Few-Shot) | 17.92 |
| Note: RepGen leverages a learning-enhanced context and iterative refinement. | |
Real-World Developer Impact
A controlled developer study with 27 participants demonstrated RepGen's practical utility, improving success rates by 23.35% and reducing reproduction time by 56.8%. This translates to lower cognitive load for developers and faster bug resolution.
23.35%
Improvement in Developer Success Rate
56.8%
Reduction in Time to Reproduce
Understanding Non-Reproducible Bugs
RepGen's current limitations primarily involve API/Dependency bugs, environment-dependent bugs, and data-dependent bugs, which rely on highly specific external conditions beyond current LLM capabilities.
Categories of Non-Reproducible DL Bugs
Our analysis of the 21 bugs RepGen failed to reproduce revealed three primary categories:
- API/Dependency Bugs: Errors due to incompatibility between code and dependencies (e.g., library version changes). GPT-4.1 showed some improvement here, resolving 5 out of 10 such bugs.
- Environment-Dependent Bugs: Bugs appearing in specific hardware or software configurations (e.g., multi-GPU, distributed computing). These remain challenging due to complex infrastructure requirements.
- Data-Dependent Bugs: Errors triggered by issues with input data (e.g., missing files, incorrect paths, formatting). Automated techniques struggle with external file system content.
Further research will focus on improving the quality of context provided to LLM agents to address these challenges.
Calculate Your AI ROI Potential
See how RepGen can significantly reduce operational costs and reclaim valuable developer hours for your enterprise.
Estimated Annual Savings
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Developer Hours Reclaimed
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Future Roadmap: Expanding RepGen's Capabilities
Our commitment to advancing DL bug reproduction continues with a strategic roadmap focusing on enhanced integration and broader support.
Distributed Training Debugging
Extend support for complex distributed training bugs and environments, a critical area for large-scale AI deployments.
Integration with Existing Tools
Seamlessly integrate with popular bug localization and repair platforms to provide an end-to-end debugging solution.
Enhanced Contextual Awareness
Further improve LLM's understanding of intricate hardware and data pipelines, vital for resolving environment and data-dependent bugs.
Ready to Transform Your DL Reliability?
Connect with our experts to discuss how RepGen can be tailored to your enterprise's specific needs and achieve verifiable bug reproduction.
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