View a PDF of the paper titled GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics, by Arsham Gholamzadeh Khoee and 4 other authors
Abstract: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.
Submission history
From: Arsham Gholamzadeh Khoee [view email]
[v1]
Thu, 27 Mar 2025 17:48:32 UTC (1,574 KB)
[v2]
Fri, 1 Aug 2025 21:33:50 UTC (823 KB)
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#ReasoningEnhanced #LLM #Agent #Automotive #Software #Release #Analytics