This case study from Uber demonstrates a practical application of generative AI in production software engineering, specifically addressing the persistent challenge of resource leaks in Java applications. The case study is particularly noteworthy as it shows how GenAI can be integrated into existing development workflows to solve real-world problems while maintaining high quality standards and safety guarantees. At its core, FixrLeak represents a sophisticated LLMOps implementation that combines traditional software engineering practices with modern AI capabilities. The system demonstrates several key aspects of successful LLM deployment in production: **System Architecture and Integration** FixrLeak's architecture showcases a well-thought-out approach to integrating GenAI into existing development tools and workflows. The system starts with SonarQube for initial leak detection, then uses Tree-sitter for code parsing and AST analysis, before finally leveraging GPT-4 for fix generation. This multi-stage pipeline ensures that the AI model operates within well-defined constraints and only attempts fixes on appropriate cases. **Safety and Validation** One of the most impressive aspects of FixrLeak's implementation is its focus on safety. The system employs several layers of validation: * AST-level analysis to ensure fixes are only attempted on safe cases where resources don't escape their function scope * Pre-submission validation including successful builds and test runs * SonarQube re-verification to confirm leak resolution * Human review as a final safety check This multi-layered approach to validation is crucial for production AI systems, especially when dealing with code modifications. **Prompt Engineering and Model Usage** The case study demonstrates sophisticated prompt engineering practices, though specific details of the prompts are not provided. The system crafts targeted prompts based on the analyzed code context and desired fix patterns, particularly focusing on modern Java practices like try-with-resources. This shows how domain knowledge can be effectively encoded into prompts to guide LLM outputs. **Production Results and Metrics** The results demonstrate impressive real-world performance: * Out of 124 initial cases, 112 were in active code * 102 cases were eligible after AST analysis * 93 cases were successfully fixed * This represents a >91% success rate on eligible cases These metrics show both the system's effectiveness and the importance of proper case filtering and validation in production AI systems. **Integration with Developer Workflow** FixrLeak is fully integrated into Uber's development workflow: * Automated pull request generation * Integration with existing build and test systems * One-click review process for developers * Periodic running to catch new resource leaks This integration demonstrates how AI systems can be made part of regular development processes rather than standing as separate tools. **Limitations and Future Improvements** The case study is transparent about current limitations, including: * Only handling intra-procedural cases (resources within single functions) * Reliance on SonarQube for initial detection * Limited to Java codebase currently Future plans include expanding to inter-procedural analysis, adding GenAI-based leak detection, and supporting additional languages like Golang. This roadmap shows a mature approach to expanding AI capabilities incrementally. **Technical Implementation Details** The implementation combines several technical components: * Tree-sitter for code parsing and AST generation * Deterministic hashing for leak tracking * GPT-4 for code generation * Integration with build and test systems * Pull request automation This technical stack shows how modern AI can be effectively combined with traditional software engineering tools and practices. **Best Practices and Lessons Learned** The case study offers valuable insights for other organizations looking to implement similar systems: * Focus on well-scoped, high-confidence fixes first * Implement thorough validation at multiple stages * Maintain human oversight while automating routine tasks * Use structured code analysis to ensure safety * Integrate with existing development workflows The system's success at Uber demonstrates that GenAI can be effectively deployed in production environments when properly constrained and validated. The focus on solving a specific, well-defined problem (resource leaks) rather than attempting more general code fixes likely contributed to its high success rate. **Production Considerations** The case study highlights several important production considerations: * The need for thorough testing and validation * Integration with existing tools and workflows * Handling of edge cases and failures * Scaling across large codebases * Maintaining developer trust through transparency Overall, this case study provides a comprehensive example of how to successfully deploy GenAI in a production software engineering environment, balancing automation with safety and quality considerations. The systematic approach to validation and integration, combined with clear metrics and limitations, makes this a valuable reference for other organizations looking to implement similar systems.