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ORCIDACM-REP 2024: Rennes, France
- Proceedings of the 2nd ACM Conference on Reproducibility and Replicability, ACM REP 2024, Rennes, France, June 18-20, 2024. ACM 2024
- Nichole Boufford
, Joseph Wonsil, Adam Craig Pocock, Jack Sullivan, Margo I. Seltzer, Thomas Pasquier:
Computational Experiment Comprehension using Provenance Summarization. - Timothée Mathieu, Philippe Preux:
Statistical comparison in empirical computer science with minimal computation usage. - Timo Breuer, Maria Maistro:
Toward Evaluating the Reproducibility of Information Retrieval Systems with Simulated Users. - Mathieu Acher, Benoît Combemale, Georges Aaron Randrianaina, Jean-Marc Jézéquel:
Embracing Deep Variability For Reproducibility and Replicability. - Ludovic Courtès, Timothy Sample, Stefano Zacchiroli, Simon Tournier:
Source Code Archiving to the Rescue of Reproducible Deployment. - Lázaro Costa, Susana Barbosa, Jácome Cunha:
Evaluating Tools for Enhancing Reproducibility in Computational Scientific Experiments. - Christian Gilbertson, Miranda Mundt, Joshua B. Teves, Simone Toribio, Reed Milewicz:
Towards Evidence-Based Software Quality Practices for Reproducibility: Practices and Aligned Software Qualities. - Guineng Zheng, Robert Ricci, Vivek Srikumar:
LogFlux: A Software Suite for Replicating Results in Automated Log Parsing. - Gaël Vila, Emmanuel Medernach, Inés Gonzalez Pepe, Axel Bonnet, Yohan Chatelain, Michaël Sdika, Tristan Glatard, Sorina Camarasu-Pop:
The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging. - Samuel Grayson, Faustino Aguilar, Reed Milewicz, Daniel S. Katz, Darko Marinov:
A benchmark suite and performance analysis of user-space provenance collectors. - Rochana R. Obadage, Sarah Michele Rajtmajer, Jian Wu:
SHORT: Can citations tell us about a paper's reproducibility? A case study of machine learning papers. - Adhithya Bhaskar, Victoria Stodden:
Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines. - Yantong Zheng, Victoria Stodden:
The Idealized Machine Learning Pipeline (IMLP) for Advancing Reproducibility in Machine Learning. - Quentin Guilloteau, Florina M. Ciorba, Millian Poquet, Dorian Goepp, Olivier Richard:
Longevity of Artifacts in Leading Parallel and Distributed Systems Conferences: a Review of the State of the Practice in 2023. - Michael Arbel, Alexandre Zouaoui:
MLXP: A framework for conducting replicable experiments in Python.
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