System-Level Test (SLT) has been a part of the test flow for integrated
circuits for over a decade and still gains importance. However, no systematic
approaches exist for test program generation, especially targeting
non-functional properties of the Device under Test (DUT). Currently, test
engineers manually compose test suites from off-the-shelf software,
approximating the end-user environment of the DUT. This is a challenging and
tedious task that does not guarantee sufficient control over non-functional
properties. This paper proposes Large Language Models (LLMs) to generate test
programs. We take a first glance at how pre-trained LLMs perform in test
program generation to optimize non-functional properties of the DUT. Therefore,
we write a prompt to generate C code snippets that maximize the instructions
per cycle of a super-scalar, out-of-order architecture in simulation.
Additionally, we apply prompt and hyperparameter optimization to achieve the
best possible results without further training.

使用预训练的大型语言模型（LLMs）生成测试程序以优化设备的非功能性能。

面向非功能属性生成系统级测试程序的大型语言模型

Large Language Models to Generate System-Level Test Programs Targeting  Non-functional Properties

Machine Learning (ML) is increasingly used to drive the operation of complex
distributed systems deployed on the cloud-edge continuum enabled by 5G.
Correspondingly, distributed systems' behavior is becoming more
non-deterministic in nature. This evolution of distributed systems requires the
definition of new assurance approaches for the verification of non-functional
properties. Certification, the most popular assurance technique for system and
software verification, is not immediately applicable to systems whose behavior
is determined by Machine Learning-based inference. However, there is an
increasing push from policy makers, regulators, and industrial stakeholders
towards the definition of techniques for the certification of non-functional
properties (e.g., fairness, robustness, privacy) of ML. This article analyzes
the challenges and deficiencies of current certification schemes, discusses
open research issues and proposes a first certification scheme for ML-based
distributed systems.

本文分析了目前认证方案存在的挑战和不足，讨论了开放的研究问题，并针对基于机器学习的分布式系统提出了第一个认证方案。