ACM REP ‘26 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.

Reproducibility Campaigns

• Large-scale or focused efforts to reproduce results within a specific computer science or interdisciplinary domain.
• Challenges and lessons learned from reproducing published papers or benchmark studies.
• Comparative analyses of findings across independently reproduced works.
• Methodological or infrastructural experiences in coordinating multi-paper or community-wide reproduction efforts.
• Lifecycle studies of reproduction, from initial replication to long-term maintenance and validation of results.
• Infrastructure and tooling challenges encountered during systematic reproduction exercises.

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.