Automatic Test-Case Reduction in Proof Assistants: A Case Study in Coq
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.
Bibliographic record
Abstract
As the adoption of proof assistants increases, there is a need for efficiency in identifying, documenting, and fixing compatibility issues that arise from proof-assistant evolution. We present the Coq Bug Minimizer, a tool for reproducing buggy behavior with minimal and standalone files, integrated with coqbot to trigger automatically on failures from Coq’s reverse dependency compatibility testing. Our tool eliminates the overhead of having to download, set up, compile, and then explore and understand large developments, enabling Coq developers to easily obtain modular test-case files for fast experimentation. In this paper, we describe insights about how test-case reduction is different in Coq than in traditional compilers. We expect that our insights will generalize to other proof assistants. We evaluate the Coq Bug Minimizer on over 150 compatibility testing failures. Our tool succeeds in reducing failures to smaller test cases roughly 75% of the time. The minimizer produces a fully standalone test case 89% of the time, and it is on average about one-third the size of the original test. The average reduced test case compiles in 1.25 seconds, with 75% taking under half a second.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it