Bibliographic record
Abstract
Formal verification of digital systems is achieved, today, using one of two main approaches: states exploration (mainly model checking and equivalence checking) or deductive reasoning (theorem proving). Indeed, the combination of the two approaches, states exploration and deductive reasoning promises to overcome the limitation and to enhance the capabilities of each. Our research is motivated by this goal. In this thesis, we provide the entire necessary infrastructure (data structure + algorithms) to define high level states exploration in the HOL theorem prover named as MDG-HOL platform. While related work has tackled the same problem by representing primitive Binary Decision Diagram (BDD) operations as inference rules added to the core of the theorem prover, we have based our approach on the Multiway Decision Graphs (MDGs). MDG generalizes ROBDD to represent and manipulate a subset of first-order logic formulae. With MDGs, a data value is represented by a single variable of an abstract type and operations on data are represented in terms of uninterpreted function. Considering MDGs instead of BDDs will raise the abstraction level of what can be verified using a state exploration within a theorem prover. The MDGs embedding is based on the logical formulation of an MDG as a Directed Formulae (DF). The DF syntax is defined as HOL built-in data types. We formalize the basic MDG operations using this syntax within HOL following a deep embedding approach. Such approach ensures the consistency of our embedding. Then, we derive the correctness proof for each MDG basic operator. Based on this platform, the MDG reachability analysis is defined in HOL as a conversion that uses the MDG theory within HOL. Then, we demonstrate the effectiveness of our platform by considering four case studies. Our obtained results show that this verification framework offers a considerable gain in terms of automation without sacrificing CPU time and memory usage compared to automatic model checker tools. Finally, we propose a reduction technique to improve MDGs model checking based on the MDG-HOL platform. The idea is to prune the transition relation of the circuits using pre-proved theorems and lemmas from the specification given at system level. We also use the consistency of the specifications to verify if the reduced model is faithful to the original one. We provide two case studies, the first one is the reduction using SAT-MDG of an Island Tunnel Controller and the second one is the MDG-HOL assume-guarantee reduction of the Look-Aside Interface. The obtained results of our approach offers a considerable gain in terms of heuristics and reduction techniques correctness as to commercial model checking; however a small penalty is paid in terms of CPU time and memory usage
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".