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Record W7093306598 · doi:10.1109/sbft66712.2025.00007

SBFT Tool Competition 2025 – UAV Testing Track

2025· article· en· W7093306598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsContext (archaeology)Competition (biology)Track (disk drive)Field (mathematics)Test (biology)SoftwareRanking (information retrieval)

Abstract

fetched live from OpenAlex

Unmanned Aerial Vehicles (UAVs) are complex and critical cyber-physical systems, making simulation-based testing essential for ensuring their safe operation. Despite its importance, this field remains relatively unexplored, offering significant opportunities for further research and advancement. The UAV Testing Competition strives to actively engage the software testing community in establishing UAV testing as a rapidly emerging and indispensable domain. The second edition of the tool competition was organized jointly by SBFT and ICST, and received five submissions in total. Two of the submitted tools competed in the context of SBFT, and their test generation capabilities were assessed across three case studies and compared against each other and the baseline approach. The final ranking was determined based on the tools’ effectiveness in failure detection and the diversity of the generated test cases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.202
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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