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 diagnostic findings before deciding what to improve

When Not to Use

• User wants to write new tests from scratch (use code-testing-agent)
• 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 raw .NET coverage collection (use run-tests), non-.NET coverage collection or analysis (use native tooling), project-wide .NET coverage/CRAP metrics (use coverage-analysis), or named-target .NET CRAP (use crap-score)
• User asks whether tests would catch a bug or wants behavioral/pseudo-mutation gaps (use test-gap-analysis)
• User wants test-mix or happy-vs-error-path classification, standardized tagging, or trait/category distributions (use test-tagging)
• 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.

Before drafting the report, make a private completeness ledger with one row for every test method and every class-level fixture/resource. Record its oracle (or absence), exception handling, state/time dependencies, and disposition. Do not publish until every row is either attached to a finding or explicitly judged sound. In particular:

• actual != oldValue is a weak mutation oracle: it accepts every wrong new value. Require the exact expected value.
• Include unused or undisposed class-level resources; method-only scans miss fields such as a static HttpClient.
• When production code is supplied, note obvious untested contracts adjacent to a finding, but do not perform exhaustive branch or mutation analysis. Route that broader question to test-gap-analysis.

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	The expected value is computed from the same actual value, such as Assert.AreEqual(dto.Name, dto.Name), Assert.AreEqual(result, result), or equivalents. Do not apply this label merely because a valid identity, clone, serialization, or round-trip contract compares output with input: those assertions can fail. Instead check whether the input exercises a transformation and whether independently known representation, field, reference-identity, or invalid-input assertions are missing.
Swallowed exceptions	try { ... } catch { }, catch (Exception) without rethrowing or asserting (.NET); bare except: or except Exception: with pass (Python); try { ... } catch (e) {} (JS/TS/Java); defer recover() without re-panic and no assertion (Go); rescue StandardError with no assertion (Ruby); Result::unwrap_or(...) swallowing errors in a test (Rust); empty catch block (Kotlin/Swift).
Assert in catch block only	try { Act(); } catch (Exception ex) { Assert.Fail(ex.Message); } (and equivalents in other languages) -- use Assert.ThrowsException / pytest.raises / expect(fn).toThrow / assertThrows / assert.Error(t, err) / #[should_panic] / Should -Throw / EXPECT_THROW instead. The test passes when no exception is thrown even if the result is wrong.
Always-true assertions	Assert.IsTrue(true), Assert.AreEqual(x, x), assert True, expect(true).toBe(true), assert.True(t, true), assert!(true), or conditions that can never fail.
Commented-out assertions	Assertions that were disabled but the test still runs, giving the illusion of coverage.

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/lazy_static!/OnceCell (Rust), $script: variables (PowerShell).
Over-mocking	More mock setup lines than actual test logic. Verifying exact call sequences on mocks rather than outcomes. Mocking types the test owns. Per language: Moq/NSubstitute/FakeItEasy (.NET), unittest.mock / pytest-mock (Python), Jest auto-mocks / Sinon (JS/TS), Mockito/PowerMock (Java), gomock/testify mock (Go), RSpec mocks/mocha (Ruby), mockall (Rust), MockK (Kotlin), Mock cmdlet (Pester), gmock (C++). For a deep mock audit in .NET, use exp-mock-usage-analysis.
Implementation coupling	Testing private methods via reflection (MethodInfo.Invoke, getattr in Python, (thing as any) in TS, Field.setAccessible(true) in Java, Object#send in Ruby, internal pub(crate) access in Rust). Asserting on internal state instead of observable behavior. Verifying exact method call counts on collaborators instead of business outcomes.
Broad exception assertions	Assert.ThrowsException<Exception>(...) (.NET) / pytest.raises(Exception) / expect(fn).toThrow(Error) without a message matcher / assertThrows(Exception.class, ...) (Java) / assert.Error(t, err) without checking the kind / expect { ... }.to raise_error without class (RSpec) / #[should_panic] without expected = "..." / Should -Throw without -ExpectedMessage / EXPECT_ANY_THROW instead of EXPECT_THROW(stmt, SpecificType).

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	Assert.AreEqual(expected, actual, "Expected and actual are not equal") / assert x == y, "x is not equal to y" / assertEquals(x, y, "values not equal") add no information. Messages should describe the business meaning.
Missing AAA / Given-When-Then separation	Arrange/Act/Assert (or Given/When/Then for BDD frameworks like RSpec, Kotest behavior specs, Pester) phases are interleaved or indistinguishable.

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.
• Use the caller's severity vocabulary consistently. If the caller asks for Critical / Warning / Info, map reliability risks to Warning and maintenance/cosmetic concerns to Info rather than silently collapsing every item into Critical. Severity describes the demonstrated failure mode, not how much prose a finding receives.
• Separate a systemic finding from its instances. Coverage touching across a facade is one Critical systemic finding whose evidence lists every affected test. All assertion-free instances, including the last facade method, retain the same false-confidence severity. Report 1 finding / 6 affected tests, not six findings plus a seventh summary finding, and do not downgrade one instance merely to manufacture multiple tiers.
• Do not severity-rank ordinary missing cases as anti-patterns. Adjacent untested branches, exception paths, and boundaries may be useful coverage opportunities, but list them separately from the anti-pattern counts unless a weak existing test specifically creates the gap. They are not Critical merely because the suite has a systemic Critical issue.
• 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.
  • Round-trip or serialization equality with non-trivial input. It is valid metamorphic evidence; suggest an independent representation assertion when two implementations could share the same bug.
  • Clone value equality. Keep it, and add distinct-reference or mutation- independence evidence when the contract promises a deep copy.
  • A validator or accessor returning the original value when pass-through is the production contract. Missing invalid-input cases are a coverage gap, not proof that the existing assertion is tautological.

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 five:

1. Account for every test in scope. Build the complete method/field inventory before summarizing. For a systematic pattern such as coverage touching, enumerate every affected test at least once rather than giving representative examples. A finding table that silently skips tests (or fixtures like an unused static HttpClient field) is incomplete. State the number reviewed.
2. Verify the production contract before judging the oracle. Inspect the actual transformation, DTO fields, and promised identity/clone semantics. Never invent fields or require lossless round-tripping when production is intentionally lossy. For every suspicious equality, write down the independently known oracle before assigning a finding. If the assertion compares a transformed output with non-trivial input, clone state, snapshot, mock verification, or a framework-native assertion context, explain why it can fail before calling it tautological or assertion-free.
3. 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.
4. Name obvious adjacent gaps without widening into mutation analysis — when production code is supplied, note directly related untested throws, null results, boundary values, and round-trip/culture-sensitivity risks in an Adjacent coverage gaps section. Use test-gap-analysis for exhaustive branch-by-branch behavioral gaps.
5. 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.
6. Make non-findings decisive. For a clean or mostly clean small suite, name the suspicious constructs you cleared and the framework rule that makes each valid. Do not bury a clean verdict under a generic checklist or speculative improvements.

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.

Before publishing, assign each finding a stable identity. A grouped row counts as one finding regardless of how many methods it lists; separate rows count separately. Recompute the summary from those rows. Keep affected tests as a different number so a bundled finding cannot create a hidden count mismatch.

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)
• Identity and round-trip findings match the production contract and use only real fields
• 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
• Grouped findings distinguish finding count from affected-test count
• Adjacent coverage opportunities are not inflated into Critical anti-pattern 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