Portus: Linking Alloy with SMT-based Finite Model Finding
Bibliographic record
Abstract
Alloy is a well-known, formal, declarative language for modelling systems early in the software development process. Currently, it uses the Kodkodlibrary as a back-end for finite model finding. Kodkodtranslates the model to a SAT problem; however, this method can often handle only problems of fairly low-size sets and is inherently finite. We present Portus, a method for translating Alloy into an equivalent many-sorted first-order logic problem (MSFOL). Once in MSFOL, the problem can be evaluated by an SMT-based finite model finding method implemented in the Fortresslibrary, creating an alternative back-end for the AlloyAnalyzer. Fortressconverts the MSFOL finite model finding problem into the logic of uninterpreted functions with equality (EUF), a decidable fragment of first-order logic that is well-supported in many SMT solvers. We compare the performance of Portuswith Kodkodon a corpus of 63 Alloy models written by experts. Our method is fully integrated into the AlloyAnalyzer.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".