GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
This addresses the slow, costly, and error-prone manual analysis of tabular data for safety-critical automotive software release decisions, though it appears incremental as an enhancement to existing LLM-based automation approaches.
The paper tackles the problem of automating software release validation in automotive manufacturing by introducing GateLens, an LLM-based system that translates natural language queries into Relational Algebra expressions and optimized Python code for tabular data analysis. Experimental results show it outperforms existing systems on real-world datasets, with industrial deployment achieving over 80% reduction in analysis time while maintaining high accuracy.
Ensuring reliable software release decisions is critical in safety-critical domains such as automotive manufacturing. Release validation relies on large tabular datasets, yet manual analysis is slow, costly, and error-prone. While Large Language Models (LLMs) offer promising automation potential, they face challenges in analytical reasoning, structured data handling, and ambiguity resolution. This paper introduces GateLens, an LLM-based system for analyzing tabular data in the automotive domain. GateLens translates natural language queries into Relational Algebra (RA) expressions and generates optimized Python code. Unlike traditional multi-agent or planning-based systems that can be slow, opaque, and costly to maintain, GateLens emphasizes speed, transparency, and reliability. Experimental results show that GateLens outperforms the existing Chain-of-Thought (CoT) + Self-Consistency (SC) based system on real-world datasets, particularly in handling complex and ambiguous queries. Ablation studies confirm the essential role of the RA layer. Industrial deployment shows over 80% reduction in analysis time while maintaining high accuracy across test result interpretation, impact assessment, and release candidate evaluation. GateLens operates effectively in zero-shot settings without requiring few-shot examples or agent orchestration. This work advances deployable LLM system design by identifying key architectural features-intermediate formal representations, execution efficiency, and low configuration overhead-crucial for safety-critical industrial applications.