Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem-Driven Multi-Layer Approach
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| Natura: | Recurso digital |
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Zenodo
2025
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| contents | <p>This repository provides a fully offline, deterministic replication package for the paper <strong>“Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem-Driven Multi-Layer Approach.”</strong> It ships lightweight Python implementations of the five-layer AURORA framework (COL, SCL, CDM, RPE, RAS), synthetic datasets, deterministic logs, and scripts that regenerate all reported tables. The package mirrors the AURORA layers and reproduces every reported table from synthetic traces; it does not embed any production integrations or industrial infrastructure.</p> <p> </p> <ul> <li><strong>aurora/</strong> – Core AURORA implementations and utilities.</li> <li><strong>aut_sim/</strong> – Toy industrial simulation components and drift schedules used in the experiments.</li> <li><strong>data/</strong> – Synthetic suites, generated logs, canary scores, paper targets, and small image assets.</li> <li><strong>paper_outputs/tables/</strong> – Pre-generated LaTeX tables (IV–IX) that match the paper targets.</li> <li><strong>scripts/</strong> – Reproduction utilities, deterministic data generators, CI entrypoints, and sanity checks.</li> <li><strong>tests/</strong> – Unit and integration tests that exercise metrics, scheduling, CI artifacts, and reproduction scripts.</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18001923 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem-Driven Multi-Layer Approach Anonymous <p>This repository provides a fully offline, deterministic replication package for the paper <strong>“Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem-Driven Multi-Layer Approach.”</strong> It ships lightweight Python implementations of the five-layer AURORA framework (COL, SCL, CDM, RPE, RAS), synthetic datasets, deterministic logs, and scripts that regenerate all reported tables. The package mirrors the AURORA layers and reproduces every reported table from synthetic traces; it does not embed any production integrations or industrial infrastructure.</p> <p> </p> <ul> <li><strong>aurora/</strong> – Core AURORA implementations and utilities.</li> <li><strong>aut_sim/</strong> – Toy industrial simulation components and drift schedules used in the experiments.</li> <li><strong>data/</strong> – Synthetic suites, generated logs, canary scores, paper targets, and small image assets.</li> <li><strong>paper_outputs/tables/</strong> – Pre-generated LaTeX tables (IV–IX) that match the paper targets.</li> <li><strong>scripts/</strong> – Reproduction utilities, deterministic data generators, CI entrypoints, and sanity checks.</li> <li><strong>tests/</strong> – Unit and integration tests that exercise metrics, scheduling, CI artifacts, and reproduction scripts.</li> </ul> |
| title | Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem-Driven Multi-Layer Approach |
| url | https://doi.org/10.5281/zenodo.18001923 |