Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their labor-intensive nature. Large Language Models (LLMs) present a promising solution to these challenges. However, their application generally demands multiple rounds of human-driven prompt engineering, which limits their practical deployment, particularly for industrial end-users who need reliable and efficient results. In this paper, we propose GoNoGo, an LLM agent system designed to streamline automotive software deployment while meeting both functional requirements and practical industrial constraints. Unlike previous systems, GoNoGo is specifically tailored to address domain-specific and risk-sensitive systems. We evaluate GoNoGo's performance across different task difficulties using zero-shot and few-shot examples taken from industrial practice. Our results show that GoNoGo achieves a 100% success rate for tasks up to Level 2 difficulty with 3-shot examples, and maintains high performance even for more complex tasks. We find that GoNoGo effectively automates decision-making for simpler tasks, significantly reducing the need for manual intervention. In summary, GoNoGo represents an efficient and user-friendly LLM-based solution currently employed in our industrial partner's company to assist with software release decision-making, supporting more informed and timely decisions in the release process for risk-sensitive vehicle systems.

This work provides documentation for a suite of R functions contained in gonogo.R. The functions provide sensitivity testing practitioners and researchers with an ability to conduct, analyze and simulate various sensitivity experiments involving binary responses and a single stimulus level (e.g., drug dosage, drop height, velocity, etc.). Included are the modern Neyer and 3pod adaptive procedures, as well as the Bruceton and Langlie. The latter two benchmark procedures are capable of being performed according to generalized up-down transformed-response rules. Each procedure is designated phase-one of a three-phase experiment. The goal of phase-one is to achieve overlapping data. The two additional (and optional) refinement phases utilize the D-optimal criteria and the Robbins-Monro-Joseph procedure. The goals of the two refinement phases are to situate testing in the vicinity of the median and tails of the latent response distribution, respectively.