Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929281935867904 |
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| author | Schwachhofer, Denis Domanski, Peter Becker, Steffen Wagner, Stefan Sauer, Matthias Pflüger, Dirk Polian, Ilia |
| author_facet | Schwachhofer, Denis Domanski, Peter Becker, Steffen Wagner, Stefan Sauer, Matthias Pflüger, Dirk Polian, Ilia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10086 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties Schwachhofer, Denis Domanski, Peter Becker, Steffen Wagner, Stefan Sauer, Matthias Pflüger, Dirk Polian, Ilia Software Engineering Artificial Intelligence Emerging Technologies Programming Languages 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. |
| title | Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties |
| topic | Software Engineering Artificial Intelligence Emerging Technologies Programming Languages |
| url | https://arxiv.org/abs/2403.10086 |