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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.

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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.

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

996 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
996 works in the cohort · of 4,299,418page 13 of 20

Labels cover 2 of 996 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 996 of 996 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.

affunlabeled
Feature-Driven End-to-End Test Generation
Parsa Alian, Noor Nashid, Mobina Shahbandeh, Taha Shabani, Ali Mesbah
2025· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Relevant empirical testing research
James H. Andrews
2004· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Exceptions for Dependability
Emil Sekerinski
2011· book-chapter· en· Advances in computer and electrical engineering book series· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Structural Coverage for Lotos
Daniel Amyot, Luigi Logrippo
2000· book-chapter· en· IFIP advances in information and communication technology· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Genetic Improvement @ ICSE 2020
William B. Langdon, Westley Weimer, Justyna Petke, Erik M. Fredericks, Seongmin Lee, Emily Winter +10 more
2020· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affunlabeled
A testing framework for JADE agent-based software
A Kalache, Mourad Badri, Farid Mokhati, Mohamed Chaouki Babahenini
2023· article· en· Multiagent and Grid Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Galápagos: Automated N-Version Programming with LLMs
Javier Ron, Diogo Gaspar, Javier Cabrera-Arteaga, Benoît Baudry, Martin Monperrus
2025· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Build Notifications in Agile Environments
Ruth Ablett, Frank Maurer, Ehud Sharlin, Jörg Denzinger, Craig Schock
2008· book-chapter· en· Lecture notes in business information processing· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
MINTS: Unsupervised Temporal Specifications Miner
Pradeep Kumar Mahato, Apurva Narayan
2021· article· en· 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Testing Updated Apps by Adapting Learned Models
Chanh Duc Ngo, Fabrizio Pastore, Lionel Briand
2024· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
A Combined Concept Location Method for Java Programs
Dapeng Liu, Shaochun Xu
2007· article· en· Proceedings - International Computer Software & Applications Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affno abstractunlabeled
Towards a formalization of viewpoints testing
Marius C. Bujorianu, Savitri Maharaj, Manuela L. Bujorianu
2002· article· en· Kent Academic Repository (University of Kent)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Model-Based Test Cases Reuse and Optimization
Mohamed Mussa, Ferhat Khendek
2018· book-chapter· en· Advances in computers· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
ZigZagFuzz: Interleaved Fuzzing of Program Options and Files
Ahcheong Lee, Y.K. Choi, Shin Hong, Yunho Kim, Kyutae Cho, Moonzoo Kim
2024· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
API Conformance Verification for Java Programs
Xin Li, H. James Hoover, Piotr Rudnicki
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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