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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.02166 |
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| _version_ | 1866911189183758336 |
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| author | Mbodji, Fatou Ndiaye Diallo, El-hacen Samhi, Jordan Liu, Kui Klein, Jacques Bissyande, Tegawendé F. |
| author_facet | Mbodji, Fatou Ndiaye Diallo, El-hacen Samhi, Jordan Liu, Kui Klein, Jacques Bissyande, Tegawendé F. |
| contents | Code agents and empirical software engineering rely on public code datasets, yet these datasets lack verifiable quality guarantees. Static 'dataset cards' inform, but they are neither auditable nor do they offer statistical guarantees, making it difficult to attest to dataset quality. Teams build isolated, ad-hoc cleaning pipelines. This fragments effort and raises cost. We present SIEVE, a community-driven framework. It turns per-property checks into Confidence Cards-machine-readable, verifiable certificates with anytime-valid statistical bounds. We outline a research plan to bring SIEVE to maturity, replacing narrative cards with anytime-verifiable certification. This shift is expected to lower quality-assurance costs and increase trust in code-datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SIEVE: Towards Verifiable Certification for Code-datasets Mbodji, Fatou Ndiaye Diallo, El-hacen Samhi, Jordan Liu, Kui Klein, Jacques Bissyande, Tegawendé F. Software Engineering Artificial Intelligence Code agents and empirical software engineering rely on public code datasets, yet these datasets lack verifiable quality guarantees. Static 'dataset cards' inform, but they are neither auditable nor do they offer statistical guarantees, making it difficult to attest to dataset quality. Teams build isolated, ad-hoc cleaning pipelines. This fragments effort and raises cost. We present SIEVE, a community-driven framework. It turns per-property checks into Confidence Cards-machine-readable, verifiable certificates with anytime-valid statistical bounds. We outline a research plan to bring SIEVE to maturity, replacing narrative cards with anytime-verifiable certification. This shift is expected to lower quality-assurance costs and increase trust in code-datasets. |
| title | SIEVE: Towards Verifiable Certification for Code-datasets |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.02166 |