Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers
Artifact Evaluation (AE) is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data meet safety-critical actuation. Yet, manual reproducibility checks are time-consuming and do not scale with growing submission volumes. In this work, we demonstrate that Large Language Models (LLMs) can provide powerful support for AE tasks: (i) text-based reproducibility rating, (ii) autonomous sandboxed execution environment preparation, and (iii) assessment of methodological pitfalls. Our rep...
| Authors | David Heye, Karl Kindermann, Robin Decker et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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