[2503.21735] GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics

Computer Science > Software Engineering arXiv:2503.21735 (cs) [Submitted on 27 Mar 2025 (v1), last revised 1 Mar 2026 (this version, v3)] Title:GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics Authors:Arsham Gholamzadeh Khoee, Shuai Wang, Yinan Yu, Robert Feldt, Dhasarathy Parthasarathy 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 View PDF HTML (experimental) Abstract:Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domains relies on large tabular datasets, where 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 architecture for reliable analysis of complex tabular data. Its key innovation is the use of Relational Algebra (RA) as a formal intermediate representation between natural-language reasoning and executable code, addressing the reasoning-to-code gap that can arise in direct generation approaches. In our automotive instantiation, GateLens translates natural language queries into RA expressions and generates optimized Python code. Unlike traditional multi-agent or planning-based sys...