UPBEAT: Test Input Checks of Q# Quantum Libraries
Bibliographic record
Abstract
High-level programming models like Q# significantly simplify the complexity of programming for quantum computing. These models are supported by a set of foundation libraries for code development. However, errors can occur in the library implementation, and one common root cause is the lack of or incomplete checks on properties like values, length, and quantum states of inputs passed to user-facing subroutines. This paper presents Upbeat, a fuzzing tool to generate random test cases for bugs related to input checking in Q# libraries. Upbeat develops an automated process to extract constraints from the API documentation and the developer implemented input-checking statements. It leverages open-source Q# code samples to synthesize test programs. It frames the test case generation as a constraint satisfaction problem for classical computing and a quantum state model for quantum computing to produce carefully generated subroutine inputs to test if the input-checking mechanism is appropriately implemented. Under 100 hours of automated test runs, Upbeat has successfully identified 16 bugs in API implementations and 4 documentation errors. Of these, 14 have been confirmed, and 12 have been fixed by the library developers.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".