Steffen Becker

Denis Schwachhofer, Peter Domanski, Steffen Becker et al.

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.

Ilia Polian, Jens Anders, Steffen Becker et al.

System-level test, or SLT, is an increasingly important process step in today's integrated circuit testing flows. Broadly speaking, SLT aims at executing functional workloads in operational modes. In this paper, we consolidate available knowledge about what SLT is precisely and why it is used despite its considerable costs and complexities. We discuss the types or failures covered by SLT, and outline approaches to quality assessment, test generation and root-cause diagnosis in the context of SLT. Observing that the theoretical understanding for all these questions has not yet reached the level of maturity of the more conventional structural and functional test methods, we outline new and promising directions for methodical developments leveraging on recent findings from software engineering.

Vijayshree Vijayshree, Markus Frank, Steffen Becker

In the software development process, model transformation is increasingly assimilated. However, systems being developed with model transformation sometimes grow in size and become complex. Meanwhile, the performance of model transformation tends to decrease. Hence, performance is an important quality of model transformation. According to current research model transformation performance focuses on optimising the engines internally. However, there exists no research activities to support transformation engineer to identify performance bottleneck in the transformation rules and hence, to predict the overall performance. In this paper we vision our aim at providing an approach of monitoring and profiling to identify the root cause of performance issues in the transformation rules and to predict the performance of model transformation. This will enable software engineers to systematically identify performance issues as well as predict the performance of model transformation.