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ACM REP 2025: ACM Conference on Reproducibility and Replicability

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ACM REP
投稿締切日:
2025-04-07 Extended
通知日:
2025-06-23
開催日:
2025-07-29
開催地:
Vancouver, British Columbia, Canada
閲覧: 1051   フォロー: 0   参加: 0

論文募集

ACM REP 2025 (ACM Conference on Reproducibility and Replicability) is an academic conference held in Vancouver, British Columbia, Canada on 2025-07-29. The paper submission deadline is 2025-04-07 (extended). Acceptance notifications are sent on 2025-06-23.

ACM REP ‘25 welcomes submissions across computing disciplines, spanning both traditional computer science and interdisciplinary scientific computing applications in biology, chemistry, physics, astronomy, genomics, geosciences, etc. The conference particularly values submissions that demonstrate reproducible experimental results. Where full reproduction is not achieved, detailed documentation of the reproducibility experience is equally valuable. The conference addresses various aspects of reproducibility and replicability, including but not limited to the following topics: Reproducibility Concepts Experiment dependency management. Experiment portability for code, performance, and related metrics. Software and artifact packaging and container-related reproducibility methods. Approximate reproducibility. Record and replay methods. Data versioning and preservation. Provenance of data-intensive experiments. Automated experiment execution and validation. Reproducibility-aware computational infrastructure. Experiment discoverability for re-use. Approaches for advancing reproducibility. Reproducibility Experiences Experience of sharing and consuming reproducible artifacts. Conference-scale artifact evaluation experiences and practices. Experiences as part of hackathons and summer programs. Classroom and teaching experiences. Usability and adaptability of reproducibility frameworks into already-established domain-specific tools. Frameworks for sociological constructs to incentivize paradigm shifts. Policies around publication of articles/software. Experiences within computational science communities. Collecting datasets from laboratory / real-world settings. Systems and Security Concerns Experience comparing published systems in a domain. Tools to support replicability of system analysis. Designing machine learning workflows to support reproducibility. Reproducing real-world security findings. Privacy concerns arising from reproducibility. Challenges of reproducing security experiments. Securing reproducibility infrastructure. Broader Reproducibility Cost-benefit analysis frameworks for reproducibility. Novel methods and techniques that impact reproducibility. Reusability, repurposability, and replicability methods. Long-term artifact archiving and verification/testing for future reproducibility.
最終更新：Dou Sun（）

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