Can LLMs reliably analyze automotive release data?
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
March 28, 2025
https://arxiv.org/pdf/2503.21735GateLens uses LLMs and relational algebra to automate the analysis of tabular data (like test results) in automotive software development, improving the speed and accuracy of release decisions. It translates natural language queries into relational algebra, then generates Python code to execute the analysis, outperforming baseline systems and reducing manual analysis time by over 80%. Key to its design is its use of relational algebra for enhanced reasoning, robust handling of diverse queries, and minimizing the exposure of sensitive data to LLMs. Its deployment demonstrates practical value in real-world industrial settings and its modularity facilitates integration with future LLM advancements.