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

• URL: http://arxiv.org/abs/2403.10086v2
• Date: Tue, 19 Mar 2024 09:30:21 GMT
• Title: Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties
• Authors: Denis Schwachhofer, Peter Domanski, Steffen Becker, Stefan Wagner, Matthias Sauer, Dirk Pflüger, Ilia Polian,
• Abstract summary: 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.
• Score: 3.3305233186101226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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