Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts
Bibliographic record
Abstract
This dataset contains the code and instructions needed to replicate the experimental results presented in the TACAS 2018 paper "Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts". The problem of program synthesis - the definition of processes to automatically derive implementations that are guaranteed to comply with specifications expressed in the form of logic formulas - is an increasingly well-studied area. Here and in the accompanying paper, a novel approach to automate program synthesis using a validity-guided technique and an Assume-Guarantee convention for specifications is demonstrated. This approach is efficient, general and completely automated, with no requirement for the templates or user guidance relied upon by existing techniques such as k-induction. The implementation of this novel algorithm for the synthesis of reactive systems, named JSyn-vg, has been added to a branch of the JKind model checker. This implementation, the benchmarks used to demonstrate its effectiveness as well as other dependencies are provided here. Further details regarding the benchmarks and instructions need to reproduce the results in the accompanying paper are provided in README.txt.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.034 |
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".