dubzzz/fast-check

Features

• Arbitrary Data Generation - Implements type-driven arbitrary data generation to verify software invariants across all possible inputs.
• Deterministic Random Data Generation - Produces a wide variety of reproducible random data types, from primitives to complex objects, for property-based testing.
• Fuzz Testing - Executes predicates against high volumes of random inputs to uncover critical bugs and edge cases.
• Property-Based Fuzzing - Provides a property-based testing engine that generates random input sequences to verify software invariants and uncover edge cases.
• Property-Based Testing - Implements a property-based testing framework to verify software invariants using randomized input data.
• Generative Testing - Implements a generative testing framework to verify software invariants by producing a wide range of randomized inputs.
• Random Value Generators - Provides a comprehensive system for generating randomized test data of various types to verify software invariants.
• Recursive Data Generation - Limits the depth of generated recursive structures to bias the probability of producing smaller instances.
• Task Schedulers - Ensures asynchronous tasks run within a specific execution context to deterministically simulate race conditions.
• Task Synchronization - Waits for specific task completions or idle states to synchronize asynchronous execution during tests.
• Integer Range Generators - Generates integer values constrained within specific boundaries to verify range-dependent logic.
• Floating-Point Precision Utilities - Generates double-precision floating-point numbers with range constraints to test numerical stability.
• Deterministic Runners - Provides a deterministic execution environment to manage task scheduling and identify non-deterministic race conditions.
• Model-Based Verification - Verifies system consistency by comparing real system transitions against a theoretical reference model.
• Recursive Data Generators - Enables the definition of complex data structures where types refer to each other recursively.
• Asynchronous Task Schedulers - Provides a promise scheduler to control the execution order of asynchronous operations to uncover concurrency bugs.
• Asynchronous Property Testing - Executes property-based tests where the verification logic involves asynchronous operations.
• Automated Test Data Generation - Produces complex, constrained, and recursive datasets and mock objects to seed property-based test suites.
• Concurrency Race Condition Testing - Ships a concurrency testing engine that uses task-based scheduling to simulate and detect race conditions.
• Consistency Model Verifiers - Implements model-based testing to verify that a real system's behavior remains consistent with a theoretical reference model.
• Deterministic Seed Replays - Captures the seed and input path from a failed run to exactly reproduce a specific failure.
• Invariant Verification - Runs a property against generated inputs and signals an error if a failure is detected.
• Model Testing - Implements model-based testing by comparing real system state transitions against a theoretical reference model using random command sequences.
• Numerical Array Generation - Generates randomized arrays of 64-bit signed integers for testing numeric computations.
• Property-Based Fuzzers - Functions as a test data fuzzer that executes predicates against high volumes of random inputs to uncover critical bugs.
• Property Verification - Generates wide ranges of input values to test if a logical property remains true across all cases.
• Random Number Generation - Allows capturing the internal state of the random number generator to exactly replicate failing test sequences.
• Typed Array Generators - Creates arrays of 16-bit unsigned integers with optional constraints on length and content.
• Test Case Generators - Produces diverse values of specific types and nested structures for use in property-based testing.
• Generator Combinators - Creates new data generators by mapping, chaining, or filtering values from a base generator.
• Value Constraints - Provides capabilities to apply specific limits and requirements to data produced by arbitrary generators.
• Weighted Selection Generators - Assigns probabilities to different generators to control the frequency of value types during testing.
• Race Condition Detection - Schedules asynchronous callbacks in different interleavings to deterministically uncover concurrency bugs.
• Specification-Compliant Data Generators - Produces a wide range of valid values including edge cases defined by a technical specification.
• State Machine Testing - Provides model-based testing to verify system consistency using randomized sequences of interdependent commands.
• Test Case Shrinking - Simplifies complex failing inputs into the smallest possible counterexample to isolate the cause of failure.
• Asynchronous Task Scheduling - Controls the execution order of asynchronous tasks to deterministically uncover race conditions and concurrency bugs.
• Text and String Generation - Generates random strings based on constraints to test system robustness against various text inputs.
• State-Based Model Testing Tools - Implements a state-based model testing engine to verify system consistency via random command sequences.
• Array Length Constraints - Defines the required and optional length boundaries for generated subarrays to ensure specific size requirements.
• Unique Identifier Generators - Produces sortable, unique identifiers conforming to the ULID specification for test data generation.
• Command Sequence Generation - Produces scenarios of executable actions that can be shrunk to isolate the minimal steps required to trigger a failure.
• Structured JSON Generation - Enforces structural rules and limits on the content of generated JSON objects.
• JSON Value Generators - Creates random values compatible with JSON parsing, including strings, numbers, and nested structures.
• Relational Data Generation - Creates a graph of related entities based on a schema definition to test complex relational data.
• Schema-Adherent Generation - Defines structural requirements for generated objects to ensure they adhere to specific schemas.
• JSON Content Generators - Generates random JSON strings and structured content based on provided constraints for testing.
• Encoded String Generators - Produces random strings encoded in base64 format while ensuring valid output lengths.
• Standard Email Generators - Produces random string values that conform to email address standards for property-based testing.
• Synthetic Network Address Generators - Produces randomized but valid IPv4 address strings for network-related test scenarios.
• Unsigned Integer Array Generators - Generates arrays of unsigned 32-bit integers as randomized inputs for property tests.
• Uint8Array Generators - Produces typed arrays of 8-bit unsigned integers to simulate various binary data inputs.
• Model-Based Record Generators - Produces objects with dynamic keys and values based on provided models to test varying data shapes.
• Structured Random Data Generation - Produces objects with random keys and values to test how a system handles various data shapes.
• Comparator Generators - Produces functions that determine the relative order of two inputs for testing sorting or ordering logic.
• Input Distribution Analysis - Categorizes test inputs using classification functions and logs the resulting distribution statistics.
• Random Date & Time Generators - Creates random Date objects within specified ranges to verify time-dependent software logic.
• Temporal Constraints - Limits the range and precision of generated date values to ensure they fall within specific windows.
• Weighted Random Sampling - Allows assigning probabilities to multiple generators to control the frequency of specific value types during test execution.
• Model-Based Async Commands - Creates asynchronous actions that transition a model state and execute real-world operations.
• State Transition Testing - Executes sequences of operations against a stateful system to find command orders that trigger bugs.
• Scheduled Model Verification - Processes model execution through a task scheduler to verify state transitions under asynchronous interleavings.
• Random Seed Replayers - Provides deterministic seed replay to exactly reproduce specific test failures and reach the minimal counterexample.
• Recursion Depth Tracking - Prevents infinite loops in nested data structures by tracking generation depth and using lazy builders.
• Model-Based Command Interfaces - Implements a command interface to model transitions and verify system state through sequences of operations.
• Test Runner Configurations - Provides centralized configuration for default execution settings and hooks across all property runners.
• Asynchronous Model Testing - Executes a sequence of asynchronous commands against a model and a real system to verify consistency.
• Constant Value Generators - Produces fixed predefined values to ensure specific edge cases or constants are consistently exercised.
• Fixed-Length Tuple Generators - Produces arrays of a fixed length where each element is generated according to specific rules.
• Function Call Generators - Creates function calls with random arguments based on provided input definitions.
• Continuous Fuzzing - Runs properties in a loop across multiple executions to hunt for counterexamples beyond standard budgets.
• Test Configuration - Allows configuration of property test parameters, including iteration counts and counterexample search settings.
• Stateful Command Definitions - Provides primitives to define synchronous commands and assertions for verifying stateful system behavior.
• Execution Tree Analyzers - Summarizes the execution tree, including runs and shrinks, to diagnose complex test behavior.
• Test Lifecycle Hooks - Executes asynchronous setup and teardown logic before and after test iterations to maintain environment state.
• Infinite Sequence Generators - Produces infinite sequences of values to facilitate testing of long-running software processes.
• Arbitrary Numeric Arrays - Produces arrays of large integers based on specified constraints to verify properties across numeric inputs.
• Arbitrary Array Generators - Produces arrays of random values based on specific types and constraints to test list-processing functions.
• Unique Array Generators - Produces arrays containing distinct elements by ensuring no duplicate values are generated.
• Array Shape Constraints - Allows limiting the length and structure of generated arrays to meet specific property requirements.
• Random Map Generators - Creates randomized maps with keys and values derived from specified generators.
• Relational Data Generators - Creates complex sets of interdependent entities with defined relations to simulate database-like structures.
• Generated Input Filtering - Includes precondition-based input filtering to discard generated values that do not meet specific validity criteria before test execution.
• Contextual State Generators - Provides a specialized value instance to maintain state across multiple generated inputs in a single run.
• Optional Value Generators - Produces either a value from a specified generator or a null-like value to test missing data handling.
• Random Record Generators - Produces randomized records of key-value pairs with optional constraints on object size.
• Subset Generators - Produces random subsets of elements from existing arrays to test functions that process partial lists.
• Typed Array Generators - Produces clamped 8-bit unsigned integer arrays to use as randomized test input values.
• Task-Based Async Schedulers - Ships a concurrency testing engine that uses task-based asynchronous scheduling to detect non-deterministic race conditions.
• Web Authority String Generators - Produces randomized web authority strings, including user information and hostnames, following RFC 3986.
• URI Segment Generators - Produces valid string segments for URIs according to specifications to test URL parsing and routing.
• UUID Generators - Produces random Universally Unique Identifiers following specifications for versions one through five.
• Model-Based Behavioral Verification - Executes asynchronous commands against a model and a real system to verify consistent behavior.
• System Consistency Verification - Executes synchronous commands against a model and a real system to ensure behaviors remain consistent.
• URL Generators - Creates randomized web URLs based on specific constraints to verify various URL patterns.
• Randomized - Produces random strings that conform to URL standards for use as test inputs.
• Utility Libraries - Property-based testing framework for TypeScript.

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