Are the Logical Foundations of Verifying Compiler Prototypes Matching user Expectations?
Bibliographic record
Abstract
Abstract The verifying compiler (VC) project proposals suggest that mainstream software developers are its targeted end-users. Like other software engineering efforts, the VC project success depends on appropriate end-user consultation. Industrial use of program assertions for the purpose of run-time assertion checking (RAC) is becoming commonplace. A likely next step on the path to VC adoption is the use of assertions in extended static checking (ESC), a fully automated form of static program verification (SPV). Unfortunately, all current VC prototypes supporting SPV, adopt a semantics which is unsound relative to the standard run-time interpretation of assertions. In this article, we report on the results of a survey in which we asked industrial developers what logical semantics they want program assertions to have, and whether consistency across RAC and SPV tools is important. Survey results indicate that developers are in favor of a semantics for assertions that is compatible with their current use in RAC.
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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 | 0.064 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".