Constrained Horn Clauses for Program Verification and Synthesis (Invited Talk)
Bibliographic record
Abstract
First Order Logic (FOL) is a powerful formalism that naturally captures many interesting decision and optimization problems. In recent years, there has been a tremendous progress in automated logic reasoning tools, such as Boolean SATisfiability Solvers and Satisfiability Modulo Theory solvers. This enabled the use of logic and logic solvers as a universal solution to many problems in Computer Science, in general, and in Program Analysis, in particular. Most new program analysis techniques formalize the desired analysis task in a fragment of FOL, and delegate the analysis to a SAT or an SMT solver. In this talk, we focus on a fragment of FOL called Constrained Horn Clauses (CHC) and the CHC solver SPACER. CHCs arise in many applications of automated verification. They naturally capture such problems as discovery and verification of inductive invariants; Model Checking of safety properties of finite- and infinite-state systems; safety verification of push-down systems (and their extensions); modular verification of distributed and parameterized systems; type inference, and many others. Using CHC separates the process of developing a proof methodology (also known as generation of Verification Condition (VC)) from the algorithmic details of deciding whether the VC is correct. Such a flexible design simplifies supporting multiple proof methodologies, multiple languages, and multiple verification tasks with a single framework, without sacrificing performance and scalability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 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".