Formally Validating a Practical Verification Condition Generator (extended version)
Bibliographic record
Abstract
A program verifier produces reliable results only if both the logic used to justify the program's correctness is sound, and the implementation of the program verifier is itself correct. Whereas it is common to formally prove soundness of the logic, the implementation of a verifier typically remains unverified. Bugs in verifier implementations may compromise the trustworthiness of successful verification results. Since program verifiers used in practice are complex, evolving software systems, it is generally not feasible to formally verify their implementation. In this paper, we present an alternative approach: we validate successful runs of the widely-used Boogie verifier by producing a certificate which proves correctness of the obtained verification result. Boogie performs a complex series of program translations before ultimately generating a verification condition whose validity should imply the correctness of the input program. We show how to certify three of Boogie's core transformation phases: the elimination of cyclic control flow paths, the (SSA-like) replacement of assignments by assumptions using fresh variables (passification), and the final generation of verification conditions. Similar translations are employed by other verifiers. Our implementation produces certificates in Isabelle, based on a novel formalisation of the Boogie language.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".