test-anti-patterns

name	test-anti-patterns
description	Audits an existing test file or suite in any language for anti-patterns and quality issues — produces a severity-ranked report (Critical/Warning/Info). INVOKE whenever asked to audit or review tests, find what's wrong with a suite, judge whether tests are any good, or check for: tests that pass but verify nothing, missing assertions, swallowed exceptions, self-comparing / tautological assertions, coverage-touching tests, broad exceptions, flaky or order-dependent tests (Thread.Sleep, DateTime.Now, shared state), duplicated tests, or magic values — in .NET, Python/pytest, TS/Jest, Java, Go, Ruby or C++. DO NOT USE FOR: writing new tests (use code-testing-agent, or writing-mstest-tests for MSTest); running tests (use run-tests); migration; assertion-diversity metrics (use assertion-quality); coverage/CRAP metrics (use coverage-analysis); the testsmells.org academic catalog (use test-smell-detection); fixing or modernizing MSTest tests, assertions, attributes, or lifecycle (use writing-mstest-tests).
license	MIT

Test Anti-Pattern Detection

Quick, pragmatic analysis of test code in any supported language for anti-patterns and quality issues that undermine test reliability, maintainability, and diagnostic value.

Language-specific guidance: Call the test-analysis-extensions skill to discover available extension files, then read the file matching the target codebase (e.g., extensions/dotnet.md, extensions/python.md, extensions/typescript.md, extensions/go.md). The extension file tells you which sleep / time / random / skip / setup-teardown / mystery-guest APIs to look for in that language.

When to Use

• User asks to review test quality or find test smells
• User wants to know why tests are flaky or unreliable
• User asks "are my tests good?" or "what's wrong with my tests?"
• User requests a test audit or test code review
• User wants to improve existing test code

When Not to Use

• User wants to write new tests from scratch (use code-testing-agent for any language, or writing-mstest-tests for MSTest specifically)
• User wants direct implementation fixes rather than a diagnostic review (use the relevant write/edit skill)
• User asks to fix swapped Assert.AreEqual argument order in MSTest (use writing-mstest-tests)
• User asks to convert MSTest DynamicData from IEnumerable<object[]> to ValueTuple (use writing-mstest-tests)
• User wants to run or execute tests (use run-tests for .NET)
• User wants to migrate between test frameworks or versions (use migration skills)
• User wants to measure code coverage (out of scope)
• User wants a deep formal test smell audit with academic taxonomy and extended catalog (use test-smell-detection)

Inputs

Input	Required	Description
Test code	Yes	One or more test files or classes to analyze
Production code	No	The code under test, for context on what tests should verify
Specific concern	No	A focused area like "flakiness" or "naming" to narrow the review

Workflow

Step 1: Detect language and load extension

Identify the target codebase's language and test framework. Call the test-analysis-extensions skill and read the matching extension file. The extension file documents framework-specific anti-pattern markers — what counts as a sleep/wait, a test marker, a skip, a setup/teardown, a shared-state hot spot, and an integration boundary — so this skill stays language-neutral.

Step 2: Gather the test code

Read the test files the user wants reviewed. If the user points to a directory or project, scan for all test files using the discovery markers in the loaded language extension file (e.g., [TestClass]/[Fact]/[Test] for .NET, test_*.py / def test_* for pytest, *.test.ts / it() for Jest, *Test.java / @Test for JUnit, *_test.go / func TestXxx for Go, *_spec.rb for RSpec, #[test] for Rust, *.Tests.ps1 / Describe for Pester, TEST(...) for GoogleTest, TEST_CASE(...) for Catch2/doctest).

If production code is available, read it too -- this is critical for detecting tests that are coupled to implementation details rather than behavior.

Step 3: Scan for anti-patterns

Check each test file against the anti-pattern catalog below. Report findings grouped by severity. The examples are .NET-centric but the patterns generalize — use the loaded language extension file to map each pattern to the framework you are auditing.

Critical -- Tests that give false confidence

Anti-Pattern	What to Look For
No assertions	Test methods that execute code but never assert anything. A passing test without assertions proves nothing. In .NET look for missing Assert.*; in pytest a function with no assert and no pytest.raises; in Jest no expect(...); in JUnit no assert*/assertThat; in Go a test that never calls t.Error*, t.Fatal*, or testify; in RSpec a block with no expect; in Pester no Should. Mock-call verifications (verify(mock), expect(mock).toHaveBeenCalled, Should -Invoke) are real assertions.
Missing await on async assertions (JS/TS, .NET, Python, Kotlin, Swift)	expect(promise).resolves.toBe(x) without await/return, pytest-asyncio test with un-awaited coroutine, async Task xUnit test calling Assert.ThrowsAsync without await, Kotest suspending test without runTest, Swift Testing async test without await. These tests silently pass even when the underlying assertion would have failed.
Coverage touching	Test class that methodically calls every public member on a type — often in alphabetical or declaration order — without asserting meaningful outcomes. Each test typically does var result = sut.MethodName(...) (or result = sut.method_name(...), sut.methodName(), sut.MethodName(t)) with no assertion, or only a trivial null/None/nil check. The intent is to inflate code-coverage metrics rather than verify behavior. Distinct from a single assertion-free test: the pattern is systematic coverage of the surface area with no real verification.
Self-referential assertion	Asserts that the output of an operation equals its input when the operation is expected to be an identity or no-op, e.g. Assert.AreEqual(input, Parse(input.ToString())), assert input == parse(str(input)), expect(parse(input.toString())).toBe(input), . Also flags / / (asserting a field against itself). The test is tautological — it can only fail if the round-trip is broken, but never verifies that a actually happened.

High -- Tests likely to cause pain

Anti-Pattern	What to Look For
Flakiness indicators	Wall-clock sleeps/waits used for synchronization: Thread.Sleep / Task.Delay (.NET), time.sleep (Python), setTimeout / await new Promise(r => setTimeout(...)) (JS/TS), Thread.sleep (Java/Kotlin), time.Sleep (Go), sleep (Ruby/Bash), std::thread::sleep (Rust), Start-Sleep (Pester), std::this_thread::sleep_for (C++). Wall-clock reads without abstraction: DateTime.Now/UtcNow, datetime.now()/datetime.utcnow(), Date.now() / new Date(), System.currentTimeMillis(), time.Now(), Time.now, Instant::now(), Date()/Date.now, Get-Date, std::chrono::system_clock::now. Unseeded randomness: new Random(), random.random()/random.randint(), Math.random(), new Random() (Java/Kotlin), rand.Int() without seed, rand (Ruby), rand::random() (Rust). Environment-dependent paths (hard-coded C:\..., /tmp/..., network hosts).
Test ordering dependency	Static/global mutable state modified across tests; setup that doesn't fully reset state ([TestInitialize], setUp, beforeEach, before(:each), BeforeEach, t.Cleanup); tests that fail when run individually but pass in suite (or vice versa). Examples per language: static fields (.NET/Java), module-level globals (Python), top-level let/const in test file (JS/TS), var package globals (Go), class variables (Ruby), static mut// (Rust), variables (PowerShell).

Medium -- Maintainability and clarity issues

Anti-Pattern	What to Look For
Poor naming	Test names like Test1, TestMethod, test, names that don't describe the scenario or expected outcome. Good naming differs by language convention — see the loaded language extension file (e.g., Add_NegativeNumber_ThrowsArgumentException for .NET, test_add_negative_number_raises_value_error for pytest, addNegativeNumber_throwsArgumentException for Java, 'adds negative number throws' for Jest descriptions, TestAdd_NegativeNumber_ReturnsError for Go).
Magic values	Unexplained numbers or strings in arrange/assert: Assert.AreEqual(42, result) / assert result == 42 / expect(result).toBe(42) -- what does 42 mean?
Duplicate tests	Three or more test methods with near-identical bodies that differ only in a single input value. Should be parametrized: [DataRow]/[Theory]/[TestCase] (.NET), @pytest.mark.parametrize (pytest), test.each / it.each (Jest/Vitest), @ParameterizedTest + @ValueSource (JUnit 5), @DataProvider (TestNG), Go table-driven tests, where / shared examples (RSpec), #[rstest] (Rust), @ParameterizedTest + @MethodSource (Kotlin), -ForEach / -TestCases (Pester), INSTANTIATE_TEST_SUITE_P (GoogleTest), SECTION / GENERATE (Catch2), TEST_CASE_TEMPLATE (doctest). For a detailed duplication analysis in .NET, use exp-test-maintainability. Note: Two tests covering distinct boundary conditions (e.g., zero vs. negative) are NOT duplicates -- separate tests for different edge cases provide clearer failure diagnostics and are a valid practice.
Giant tests	Test methods exceeding ~30 lines or testing multiple behaviors at once. Hard to diagnose when they fail.
Assertion messages that repeat the assertion

Low -- Style and hygiene

Anti-Pattern	What to Look For
Unused test infrastructure	Setup/teardown hooks that do nothing — [TestInitialize]/[SetUp]/[BeforeEach], setUp/@BeforeEach/@BeforeAll, beforeEach/beforeAll, before(:each)/before(:all), BeforeEach/BeforeAll (Pester), setUpWithError (XCTest) — and test helper methods that are never called.
Unmanaged resources	Test creates disposable/closeable resources without cleanup: HttpClient/Stream without using (.NET), file/connection without with block or try/finally (Python), FileInputStream without try-with-resources (Java), defer file.Close() missing (Go), connection without ensure (Ruby), Drop not relied on / forgotten close (Rust), missing teardown for temp files / DBs in any language.
Print debugging	Leftover Console.WriteLine / Debug.WriteLine / print() / console.log / System.out.println / fmt.Println / puts / dbg! / Write-Host / std::cout statements used during test development.
Inconsistent naming convention	Mix of naming styles in the same test class/module/file (e.g., some use Method_Scenario_Expected, others use ShouldDoSomething).

Step 4: Calibrate severity honestly

Before reporting, re-check each finding against these severity rules:

• Critical/High: Only for issues that cause tests to give false confidence or be unreliable. A test that always passes regardless of correctness is Critical. Flaky shared state is High. Missing-await on async assertions is Critical (silent pass).
• Medium: Only for issues that actively harm maintainability -- 5+ nearly-identical tests, truly meaningless names like Test1 / test / it1.
• Low: Cosmetic naming mismatches, minor style preferences, assertion messages that could be better. When in doubt, rate Low.
• Not an issue (per-language nuance):
  • Go and Rust table-driven loops with sub-tests (t.Run / for case in cases { ... }) are idiomatic, not "Conditional Test Logic". Do NOT flag.
  • pytest bare assert is the canonical assertion form, not a missing assertion library. Do NOT flag.
  • Go tests use if got != want { t.Errorf(...) } as canonical equality. Do NOT flag as ad-hoc.
  • Separate tests for distinct boundary conditions (zero vs. negative vs. null). Do NOT flag as duplicates.
  • Explicit per-test setup instead of [TestInitialize] / beforeEach (this improves isolation).
  • Tests that are short and clear but could theoretically be consolidated.

IMPORTANT: If the tests are well-written, say so clearly up front. Do not inflate severity to justify the review. A review that finds zero Critical/High issues and only minor Low suggestions is a valid and valuable outcome. Lead with what the tests do well.

Step 5: Report findings

Depth bar — a tidy report that is shallower than an unassisted review is a failure. Before writing, satisfy all four:

1. Account for every test in scope. Walk the full list of test methods and fields; a finding table that silently skips tests (or fixtures like an unused static HttpClient field) is incomplete. State the number of tests reviewed.
2. Make every Critical/High fix complete and specific. Give the replacement assertion with the exact expected value (the computed discount, the exact CSV line, the full expected object), not a // assert something here placeholder.
3. Name the adjacent gaps the tests should also cover — untested error paths, boundary values, and round-trip/culture-sensitivity risks in the same class. These are part of "what's wrong with my tests", and omitting them is the most common way this review loses to an unassisted one.
4. Keep the report internally consistent. Summary counts must equal the enumerated findings. Publish a settled conclusion: do all reconsidering before you write, and never leave "wait, that's wrong" / "this should fail but doesn't" reasoning in the output.

Present findings in this structure:

1. Summary -- Total issues found, broken down by severity (Critical / High / Medium / Low). If tests are well-written, lead with that assessment.
2. Critical and High findings -- List each with:
   • The anti-pattern name
   • The specific location (file, method name, line)
   • A brief explanation of why it's a problem
   • A concrete fix (show before/after code when helpful)
3. Medium and Low findings -- Summarize in a table unless the user wants full detail
4. Positive observations -- Call out things the tests do well (sealed class, specific exception types, data-driven tests, clear AAA structure, proper use of fakes, good naming). Don't only report negatives.

Step 6: Prioritize recommendations

If there are many findings, recommend which to fix first:

1. Critical -- Fix immediately, these tests may be giving false confidence
2. High -- Fix soon, these cause flakiness or maintenance burden
3. Medium/Low -- Fix opportunistically during related edits

Validation

• Every test method in scope is accounted for (reviewed count stated; none silently skipped)
• Every finding includes a specific location (not just a general warning)
• Every Critical/High finding includes a concrete fix with exact expected values
• Adjacent untested error paths and boundary values are called out
• Summary counts match the enumerated findings
• Report covers all categories (assertions, isolation, naming, structure)
• Positive observations are included alongside problems
• Recommendations are prioritized by severity

Common Pitfalls

Pitfall	Solution
Reporting style issues as critical	Naming and formatting are Medium/Low, never Critical
Suggesting rewrites instead of targeted fixes	Show minimal diffs -- change the assertion, not the whole test
Flagging intentional design choices	If Thread.Sleep / time.sleep / time.Sleep is in an integration test testing actual timing, that's not an anti-pattern. Consider context.
Inventing false positives on clean code	If tests follow best practices, say so. A review finding "0 Critical, 0 High, 1 Low" is perfectly valid. Don't inflate findings to justify the review.
Flagging separate boundary tests as duplicates	Two tests for zero and negative inputs test different edge cases. Only flag as duplicates when 3+ tests have truly identical bodies differing by a single value.
Rating cosmetic issues as Medium	Naming mismatches (e.g., method name says ArgumentException but asserts ArgumentOutOfRangeException) are Low, not Medium -- the test still works correctly.
Ignoring the test framework	Use the terminology of the framework you loaded from the language extension; don't describe a pytest suite in MSTest terms.
Missing the forest for the trees	If 80% of tests have no assertions, lead with that systemic issue rather than listing every instance
Trading depth for tidiness	A severity table and positive observations do not substitute for coverage of every test, exact expected values in fixes, and the adjacent error-path/boundary gaps
Contradicting yourself in the report	Reason first, then write one settled verdict per finding — never emit "wait, that's wrong" / "should fail but doesn't" reconsiderations
Counts that don't add up	The summary's per-severity totals must match the findings you listed