'property-based' 검색 결과

Writes, reviews, and debugs property-based tests, which generate inputs to check general rules. Helps test conversions, parsers, normalization, and state invariants, assess whether existing assertions are meaningful, and distinguish faulty rules from implementation bugs.

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• Testing encode/decode round trips across generated inputs
• Checking normalization and sorting properties
• Reviewing whether existing property tests assert meaningful rules

Property-based testing with fast-check (TypeScript/JavaScript) and Hypothesis (Python). Generate test cases automatically, find edge cases, and test mathematical properties. Use when user mentions property-based testing, fast-check, Hypothesis, generating test data, QuickCheck-style testing, or finding edge cases automatically.

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테스트를 먼저 작성하는 흐름으로 Rust 단위·통합·비동기 테스트를 구성합니다. 다양한 입력 검증, 의존성 대체, 성능 측정과 커버리지 확인도 다룹니다.

• 테스트부터 Rust 코드 개발
• 기존 코드에 테스트 추가
• 비동기 처리와 시간 초과 검증

Property-based testing with Hypothesis for discovering edge cases and validating invariants. Use when implementing comprehensive test coverage, testing complex logic with many inputs, or validating mathematical properties and invariants across input domains. Triggered by: hypothesis, property-based testing, @given, strategies, generative testing.

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Property-based testing strategies, mutation testing, shrinking, and combined PBT+mutation workflow for test quality validation

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Property-based testing (PBT) patterns with fast-check (JS/TS), Hypothesis (Python), and gopter (Go). Generate random inputs, define invariants, shrink failures to minimal cases. Adapted from Trail of Bits. Use when testing pure functions, parsers, serializers, state machines, or any code where example-based tests miss edge cases.

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Expert guide for property-based testing (fast-check, Hypothesis) and mutation testing (Stryker, mutmut) — AI-generated test properties, invariant discovery, mutation score optimization, and test suite quality verification / Panduan ahli property-based testing (fast-check, Hypothesis) dan mutation testing (Stryker, mutmut) — properti tes yang dihasilkan AI, penemuan invarian, optimasi skor mutasi, dan verifikasi kualitas suite tes.

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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

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fast-check patterns for JS/TS — 6 canonical property categories with worked examples targeting agent-studio utilities (path normalization, safe-json, glob-to-regex, routing logic)

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Guidelines and strategies for constructing meaningful test suites, property-based tests, metamorphic relations, and fuzzing harnesses. Trigger when: - Designing verification bounds, writing tests, creating test invariants, or implementing validation gates. - Prompt contains: testing, test design, property-based testing, PBT, metamorphic testing, fuzzing, integration tests, E2E.

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Kotlin 테스트를 먼저 작성하고 Kotest와 MockK로 동작과 의존성을 검증합니다. 비동기 테스트부터 Kover를 통한 커버리지 확인까지 진행합니다.

• 기존 Kotlin 코드에 테스트 추가
• 테스트를 먼저 작성해 새 기능 개발
• 코루틴과 Flow 동작 검증

F# 단위 테스트와 통합 테스트를 작성하고 입력을 자동 생성해 속성을 검증합니다. 결과 확인, 의존성 대체, 테스트 구성과 실행을 지원합니다.

• F# 코드에 단위 테스트 추가
• 자동 생성 입력으로 속성 검증
• ASP.NET Core API 통합 테스트

Audit Solidity contracts in Foundry projects before deployment using code analysis, randomized tests, manual review, and private key checks. Produces a JSON report with findings, remediation, test coverage, and a pass or fail deployment decision.

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• Auditing Solidity contracts before deployment
• Adding fuzz and invariant tests for contracts that move funds
• Reviewing private key storage and deployment scripts

Audit Solidity contracts in Foundry projects before deployment using code analysis, randomized tests, manual review, and private key checks. Produces a JSON report with findings, remediation, test coverage, and a pass or fail deployment decision.

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• Auditing Solidity contracts before deployment
• Adding fuzz and invariant tests for contracts that move funds
• Reviewing private key storage and deployment scripts

Testing strategies including contract testing, snapshot testing, mutation testing, property-based testing, and test organization

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Write and simplify C# property-based, model-based, and executable specification tests with CsCheck. Use for generators, shrinking, stateful command sequences, Spec guards and rules, implementation conformance, or replaying generated failures.

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Port the state-delta + property-based testing paradigm to languages other than Python. DIY recipes per language; canonical Python ref shipped in nwave_ai.state_delta.

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Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD integration.

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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.

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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).

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Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

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Think and work like an expert Formal Methods Researcher. Use when a task calls for Formal Methods Researcher judgment. Reasons from operational semantics and temporal logics through SPIN/TLA+/PRISM, Coq/Lean/Isabelle, Z3/CVC5, refinement and separation logic, vacuity and false-positive diagnosis, and Dafny/F* versus property-based testing boundaries.

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Think and work like an expert Computer Scientist. Use when a task calls for Computer Scientist judgment. Reasons from computational models, abstraction contracts, invariants, and measurable complexity through CLRS-grade algorithm analysis, impossibility results (FLP, CAP, NP-hardness), property-based and chaos testing, and formal tools (TLA+, Coq, Z3) while treating partial failure, race conditions, label leakage, and abstraction leaks like GC pauses and clock skew as first-class failure modes.

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Reviews Rust test code for unit test patterns, integration test structure, async testing, mocking approaches, and property-based testing. Covers Rust 2024 edition changes including async fn in traits for mocks,

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