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Establishing Provenance Before Coding: Traditional and Next-Gen Signing.

, , , , и . CoRR, (2024)

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Helmut Schorlemmer

Heiderose Schorlemmer

Walter Schorlemmer

Sandra Schorlemmer

Uta Schorlemmer

 

Другие публикации лиц с тем же именем

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry., , , , , , , и . ICSE, стр. 2463-2475. IEEE, (2023)Establishing Provenance Before Coding: Traditional and Next-Gen Signing., , , , и . CoRR, (2024)Reusing Deep Learning Models: Challenges and Directions in Software Engineering., , , , , и . CoRR, (2024)PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages., , , , , , , и . MSR, стр. 57-61. IEEE, (2023)Signing in Four Public Software Package Registries: Quantity, Quality, and Influencing Factors., , , , , , , и . SP, стр. 1160-1178. IEEE, (2024)An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures., , , , и . SCORED@CCS, стр. 5-15. ACM, (2023)SoK: Analysis of Software Supply Chain Security by Establishing Secure Design Properties., , , и . CoRR, (2024)An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain., , , , , , , и . SCORED@CCS, стр. 105-114. ACM, (2022)Reusing Deep Learning Models: Challenges and Directions in Software Engineering., , , , , и . JVA, стр. 17-30. IEEE, (2023)SoK: Analysis of Software Supply Chain Security by Establishing Secure Design Properties., , , и . SCORED@CCS, стр. 15-24. ACM, (2022)