IC3 model checking algorithm
E909016
SAT-based model checking algorithm
formal verification technique
model checking algorithm
safety property verification technique
The IC3 model checking algorithm is a SAT-based formal verification technique that incrementally constructs inductive invariants to efficiently prove or refute safety properties of hardware and software systems.
All labels observed (7)
How this entity was disambiguated
This entity first appeared as the object of triple T11173111 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IC3 model checking algorithm Context triple: [Kenneth McMillan, knownFor, IC3 model checking algorithm]
-
A.
Symbolic Model Checking
Symbolic Model Checking is a formal verification technique that uses symbolic representations, such as binary decision diagrams, to efficiently verify properties of hardware and software systems with very large state spaces.
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B.
Model Checking (book)
"Model Checking" is a foundational textbook that systematically presents the theory and practice of using automated verification techniques to prove correctness properties of hardware and software systems.
-
C.
SPIN model checker
SPIN is a widely used open-source model checker designed for the formal verification of distributed software systems and communication protocols.
-
D.
PRISM probabilistic model checker
PRISM probabilistic model checker is a formal verification tool used to model, analyze, and verify systems that exhibit probabilistic behavior, such as randomized algorithms and communication or security protocols.
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E.
The Temporal Logic of Programs
The Temporal Logic of Programs is a landmark 1977 paper by Amir Pnueli that introduced temporal logic as a formal framework for specifying and verifying the behavior of concurrent and reactive computer programs.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: IC3 model checking algorithm Target entity description: The IC3 model checking algorithm is a SAT-based formal verification technique that incrementally constructs inductive invariants to efficiently prove or refute safety properties of hardware and software systems.
-
A.
Symbolic Model Checking
Symbolic Model Checking is a formal verification technique that uses symbolic representations, such as binary decision diagrams, to efficiently verify properties of hardware and software systems with very large state spaces.
-
B.
Model Checking (book)
"Model Checking" is a foundational textbook that systematically presents the theory and practice of using automated verification techniques to prove correctness properties of hardware and software systems.
-
C.
SPIN model checker
SPIN is a widely used open-source model checker designed for the formal verification of distributed software systems and communication protocols.
-
D.
PRISM probabilistic model checker
PRISM probabilistic model checker is a formal verification tool used to model, analyze, and verify systems that exhibit probabilistic behavior, such as randomized algorithms and communication or security protocols.
-
E.
The Temporal Logic of Programs
The Temporal Logic of Programs is a landmark 1977 paper by Amir Pnueli that introduced temporal logic as a formal framework for specifying and verifying the behavior of concurrent and reactive computer programs.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
SAT-based model checking algorithm
ⓘ
formal verification technique ⓘ model checking algorithm ⓘ safety property verification technique ⓘ |
| abbreviation | PDR ⓘ |
| acronym | IC3 ⓘ |
| advantage |
incremental and property-directed
ⓘ
often more scalable than BDD-based model checking ⓘ |
| aimsTo |
prove safety properties
ⓘ
refute safety properties ⓘ |
| alsoKnownAs |
Property Directed Reachability
ⓘ
linked to:
IC3 model checking algorithm
|
| appliedTo |
hardware systems
ⓘ
software systems ⓘ |
| basedOn |
Boolean satisfiability
ⓘ
linked to:
Boolean satisfiability problem
|
| checks | relative inductiveness of clauses ⓘ |
| comparedWith | BDD-based model checking ⓘ |
| constructs | inductive invariants ⓘ |
| coreIdea |
incrementally strengthen over-approximations of reachable states
ⓘ
maintain a sequence of clause sets approximating reachable states at increasing time frames ⓘ |
| ensures |
inductiveness of invariants
ⓘ
safety property holds in all reachable states ⓘ |
| frameRepresentation | conjunctions of clauses ⓘ |
| fullName | Incremental Construction of Inductive Clauses for Indubitable Correctness ⓘ |
| hasVariant |
IC3 for software model checking
ⓘ
IC3 for timed systems ⓘ
linked to:
IC3 model checking algorithm
IC3 with interpolation ⓘ
linked to:
IC3 model checking algorithm
parallel IC3 ⓘ |
| inputType |
safety property
ⓘ
transition system ⓘ |
| inspired | many IC3-like algorithms ⓘ |
| introducedAtConference | VMCAI 2011 ⓘ |
| introducedBy | Aaron R. Bradley ⓘ |
| introducedInYear | 2010 ⓘ |
| maintains | sequence of frames ⓘ |
| outputType |
counterexample trace
ⓘ
proof of safety ⓘ |
| refines | over-approximation of reachable states ⓘ |
| researchArea |
automated verification
ⓘ
formal methods ⓘ |
| searchStrategy |
backward reachability from bad states
ⓘ
property-directed search ⓘ |
| terminationCondition |
counterexample to safety is found
ⓘ
fixed point of frames is reached ⓘ |
| usedIn | industrial hardware verification tools ⓘ |
| uses |
SAT solving
ⓘ
clause learning ⓘ incremental SAT solving ⓘ inductive invariants ⓘ |
| verifiesPropertyType | safety properties ⓘ |
| worksOn | finite-state transition systems ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: IC3 model checking algorithm Description of subject: The IC3 model checking algorithm is a SAT-based formal verification technique that incrementally constructs inductive invariants to efficiently prove or refute safety properties of hardware and software systems.
Referenced by (9)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: IC3 model checking algorithm
linked to: IC3 model checking algorithm
linked to: IC3 model checking algorithm
linked to: IC3 model checking algorithm
IC3: Incremental Construction of Inductive Clauses for Indubitable Correctness
→
alsoKnownAs
→
IC3/PDR in hardware verification community
ⓘ
linked to: IC3 model checking algorithm
linked to: IC3 model checking algorithm
linked to: IC3 model checking algorithm