MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Software Engineering Research
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,468 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,468 works in the cohort · of 4,299,418page 54 of 70

Labels cover 10 of 3,468 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,468 of 3,468 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Rotten green tests in Java, Pharo and Python
Vincent Aranega, Julien Delplanque, Matías Martínez, Andrew P. Black, Sté́phane Ducasse, Anne Etien +2 more
2021· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
2
citations
affunlabeled
Using the GPGPU for scaling up mining software repositories
Rina Nagano, Hiroki Nakamura, Yasutaka Kamei, Bram Adams, Kenji Hisazumi, Naoyasu Ubayashi +1 more
2012· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
WIA-SZZ: Work item aware SZZ
Salomé Perez-Rosero, Robert Dyer, Samuel W. Flint, Shane McIntosh, Witawas Srisa‐an
2025· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Teaching Mining Software Repositories
Zadia Codabux, Fatemeh H. Fard, Roberto Verdecchia, Fabio Palomba, Dario Di Nucci, Gilberto Recupito
2024· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Prioritizing lingering bugs
Shirin Akbarinasaji
2018· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About