Compositional model checking
E909019
Compositional model checking is a formal verification technique that proves system correctness by analyzing components separately and then combining the results, enabling scalable verification of complex systems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Compositional model checking canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11173123 — 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: Compositional model checking Context triple: [Kenneth McMillan, notableWork, Compositional model checking]
-
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.
Temporal Verification of Reactive Systems
"Temporal Verification of Reactive Systems" is a foundational book in formal methods that presents rigorous techniques for specifying and verifying the correctness of reactive and concurrent systems using temporal logic.
-
D.
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.
-
E.
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.
- 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: Compositional model checking Target entity description: Compositional model checking is a formal verification technique that proves system correctness by analyzing components separately and then combining the results, enabling scalable verification of complex 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.
Temporal Verification of Reactive Systems
"Temporal Verification of Reactive Systems" is a foundational book in formal methods that presents rigorous techniques for specifying and verifying the correctness of reactive and concurrent systems using temporal logic.
-
D.
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.
-
E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
formal verification technique
ⓘ
model checking method ⓘ |
| aimsTo |
enable reuse of verification results
ⓘ
improve scalability of model checking ⓘ localize reasoning to components ⓘ |
| analyzes | system components separately ⓘ |
| appliedIn |
embedded systems verification
ⓘ
hardware verification ⓘ protocol verification ⓘ software verification ⓘ |
| appliesTo |
concurrent systems
ⓘ
distributed systems ⓘ reactive systems ⓘ |
| basedOn |
assume-guarantee reasoning
ⓘ
compositional reasoning ⓘ modular verification ⓘ |
| canUse |
assume-guarantee rules
ⓘ
compositional abstraction ⓘ interface automata ⓘ |
| combines | verification results of components ⓘ |
| contrastsWith | monolithic model checking ⓘ |
| dependsOn |
compositionality of system semantics
ⓘ
well-defined component interfaces ⓘ |
| developedInContextOf | state-space explosion problem ⓘ |
| enables |
component-wise refinement checking
ⓘ
incremental verification ⓘ parallel verification of components ⓘ |
| field |
computer science
ⓘ
formal methods ⓘ |
| hasPart |
component analysis
ⓘ
result composition ⓘ |
| hasProperty |
modular
ⓘ
scalable ⓘ sound under suitable assumptions ⓘ |
| relatedTo |
abstraction techniques
ⓘ
assume-guarantee model checking ⓘ compositional temporal logic reasoning ⓘ interface theories ⓘ |
| requires |
component interfaces
ⓘ
specifications of components ⓘ system decomposition ⓘ |
| usedFor |
proving system correctness
ⓘ
reducing state-space explosion ⓘ scalable verification ⓘ verifying complex systems ⓘ |
| uses |
automata-based representations
ⓘ
temporal logic specifications ⓘ transition system models ⓘ |
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: Compositional model checking Description of subject: Compositional model checking is a formal verification technique that proves system correctness by analyzing components separately and then combining the results, enabling scalable verification of complex systems.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.