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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Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI tests.
安装前可以查看交给 AI 代理的指令内容。
An example test asserts one point. A property asserts a rule over the whole input domain and lets the generator hunt for the counterexample. That trade is worth making when the code has an algebraic shape — an inverse, an invariant, an oracle — and not otherwise. Code with no such shape gets example tests; saying so is a valid outcome.
Check first whether the shape is missing or merely buried. A calculation wrapped in I/O, a string built by concatenation, an in-place mutation — each has a property and no seam to assert it through. See references/refactoring.md before concluding there is nothing to assert.
| Property | Formula | Where it applies |
|---|---|---|
| Roundtrip | decode(encode(x)) == x | Serialization, conversion pairs |
| Inverse | f(g(x)) == x | encrypt/decrypt, compress/decompress |
| Oracle | new(x) == reference(x) | Optimization, refactoring, reimplementation |
| Idempotence | f(f(x)) == f(x) | Normalization, formatting, sorting |
| Invariant | Holds before and after | Any transformation, contract state |
| Easy to verify | is_sorted(sort(x)) | Complex algorithms with cheap checkers |
| Commutativity | f(a, b) == f(b, a) | Binary and set operations |
| Associativity | f(f(a,b), c) == f(a, f(b,c)) | Combining operations |
| Identity | f(x, e) == x | Operations with a neutral element |
Strength ordering, weakest to strongest:
no crash → type preservation → invariant → idempotence → roundtrip / oracle.
Assert the strongest property the code supports. "No crash" alone rarely justifies the dependency — if that is all you can find, either a small rearrangement exposes something stronger, or the honest report is that this code is a poor PBT candidate. Rule out the first before settling for the second.
assert add(a, b) == a + b restates the implementation; no bug they
share can fail it. Pick a property that constrains the function without recomputing
it. Note the exception: f(x) == f(x) is a genuine determinism property when f
is not obviously pure — serializers over dicts or sets, hashing, anything reading
the clock.assume() that filters out nearly every input passes without
exercising anything, and self-contradictory assume() passes having run zero cases.
Push constraints into the strategy so the generator produces valid inputs directly.Load the one that matches the task in front of you:
| Task | File |
|---|---|
| Writing new tests, designing strategies | references/generating.md |
| The code has no property to assert yet | references/refactoring.md |
| Reviewing existing property tests | references/reviewing.md |
| A property test just failed | references/interpreting-failures.md |
| Library choice, Echidna and Medusa | references/libraries.md |
If the project already uses a PBT library, just write the tests in it. If it does not, adding one is a dependency decision that belongs to the user — offer it once with the specific property you would write, and take the answer either way.
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Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.
中文简介正在准备中。当前显示英语简介。
Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.
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Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.
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Reviews Algorand smart contracts written in TEAL or PyTeal for 11 vulnerability patterns. Produces a complete coverage table and findings with code references, severity ratings, and suggested fixes, with optional automated checks using Tealer.
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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.
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Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.
中文简介正在准备中。当前显示英语简介。