The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Repeatability in computer systems research
Abstract
To encourage repeatable research, fund repeatability engineering and reward commitments to sharing research artifacts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ten Simple Rules for Reproducible Computational Research
Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003285 · Cited by 880
Cited by
State of the Art: Reproducibility in Artificial Intelligence
Gundersen, O. E., & Kjensmo, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11503 · Cited by 293
Methodological Principles for Reproducible Performance Evaluation in Cloud Computing
Papadopoulos, A. V., Versluis, L., Bauer, A., Herbst, N., Kistowski, J. V., Ali-Eldin, A., Abad, C. L., Amaral, J. N., Tuma, P., & Iosup, A. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2927908 · Cited by 93
Liu, D. M., & Salganik, M. J. (2019) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023119849803 · Cited by 47
What is reproducibility in artificial intelligence and machine learning research?
Desai, A., Abdelhamid, M., & Padalkar, N. R. (2025) · AI Magazine · doi:10.1002/aaai.70004 · Cited by 42
What is Reproducibility in Artificial Intelligence and Machine Learning Research?
Desai, A., Abdelhamid, M., & Padalkar, N. R. (2024) · arXiv · doi:10.48550/arxiv.2407.10239 · Cited by 36
Understanding and improving artifact sharing in software engineering research
Timperley, C. S., Herckis, L., Le Goues, C., & Hilton, M. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-09973-5 · Cited by 22
Siegel, Z. S., Kapoor, S., Nadgir, N., Stroebl, B., & Narayanan, A. (2024) · arXiv · doi:10.48550/arxiv.2409.11363 · Cited by 4
“We manually calculated the number of reproducible studies and rounded the result: Stockemer et al. (2018), Gertler et al. (2018), Collberg & Proebsting (2016), Hardwicke et al. (2021), Raff (2019).”
Metadata
- Authors
- Collberg, C., & Proebsting, T. A.
- Venue
- Communications of the ACM
- Published
- 2016-02-25
- Volume
- 59
- Issue
- 3
- Pages
- 62-69
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2283438976
- Cited by
- 242
- References
- 1
- FWCI
- 52.99
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
