input constraints
ConceptInput constraints are conditions over inputs that define which values or structures are acceptable for a function, API, or simulation stimulus. They are central to generating valid test inputs, especially in constrained random simulation and in testing or fuzzing deep learning APIs.
First seen 7/17/2026
Last seen 7/17/2026
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Input constraints
Input constraints are restrictions on the values, types, shapes, or other properties that inputs must satisfy. In practice, they define the valid input domain for a function or system and are used to generate inputs that are accepted by the target under test.[1][3]
Role in verification and testing
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1 connectionsConstrained random simulation relies on input constraints to generate valid stimuli.
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[1] In constrained random simulation, input stimuli are randomly generated subject to declaratively specified input constraints. Stimulus generation for constrained random simulation
[2] The efficiency of constrained-random stimulus generation depends on constraint-solver performance and the distribution of generated solutions; reported work addresses mixed Boolean/integer domains with a hybrid solver. Stimulus generation for constrained random simulation
[3] Input constraints enable generation of valid inputs for deeper testing, and deep learning APIs have domain-specific input constraints often described informally in API documentation. DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
[4] DocTer extracts API parameter constraints from documentation using dependency-parse-based rules and uses the extracted constraints to generate valid and invalid inputs for testing deep learning APIs. DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
[5] DocTer achieved 85.4% precision in extracting input constraints and detected 94 bugs from 174 API functions, compared with 59 bugs for a baseline without input constraints. DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
[6] SkipFuzz learns input constraints using active learning and test-execution feedback indicating whether the library accepts or rejects an input, addressing unknown valid domains and redundant invalid inputs. SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries
[7] SkipFuzz reported 43 crashes, with 28 confirmed and 13 unique CVEs assigned. SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries