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。