Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties
<|reference_start|>Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties: 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.<|reference_end|>
@article{schwachhofer2024large,
    title={Large Language Models to Generate System-Level Test Programs Targeting
  Non-functional Properties},
    author={Denis Schwachhofer, Peter Domanski, Steffen Becker, Stefan Wagner,
  Matthias Sauer, Dirk Pfl"uger, Ilia Polian},
    journal={arXiv preprint arXiv:2403.10086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10086},
    primaryClass={cs.SE cs.AI cs.ET cs.PL}
}