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: Read the caller-provided or runtime-listed test-analysis-extensions catalog and its matching language file when needed. Do not invoke the reference-only helper. If its files are absent, use the built-in framework rules below.

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)
• 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 scope	No	Test files, classes, directory, or project to analyze. Discover from the current workspace when omitted.
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

Resolve the named test path from the current workspace before asking for input. When no path is supplied, discover test files under the current directory using the repository manifests and conventional test markers. The skill context's Base directory is documentation storage, not the user's workspace; never resolve target files relative to it.

If one reader says a path is missing but a workspace glob/search finds it, normalize that exact path and retry. Use a shell text reader (sed/cat on Unix, Get-Content on PowerShell) only for a confirmed reader availability, transport, or path-normalization failure and only after verifying the canonical path remains inside the current workspace. Stop on content-exclusion, permission/policy, workspace-boundary, or unknown failures. Audit any discovered file that a permitted reader can access; never ask the user to paste it. If every permitted reader fails, report the exact blocker without bypassing security boundaries.

Identify the language and framework. Read the matching bundled reference when needed, resolving its filename relative to the supplied catalog. Check only that known reference directory, never installation directories. If unavailable, use the catalog below and report the reference limitation.

Step 2: Gather the test code

Inventory the resolved scope before reading bodies. For one file or class, read that scope directly. For a project or suite, discover test files once, batch independent reads where tools allow, and stop when every discovered test and class-level fixture has a ledger disposition.

Use extension discovery markers when loaded; otherwise use the built-in markers in this skill (attributes such as [TestClass]/[Fact]/[Test], test_*.py, *.test.*, *_test.go, *_spec.rb, #[test], *.Tests.ps1, TEST(...), and TEST_CASE(...)).

Do not read unrelated production code wholesale. Open the production symbol corresponding to every suspicious test needed to decide whether an assertion, transformation, identity contract, or adjacent gap is real. For a systematic facade/surface-area pattern, every invoked member is relevant: read the entire small production type or inspect each invoked member, then map each weak test to the exact observable result, exception, state change, or boundary it should verify.

Step 3: Scan for anti-patterns

Check each test file against the anti-pattern catalog below. Report findings grouped by severity. Use extension mappings when loaded; otherwise use the cross-framework examples in the catalog.

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, concurrency safety, precondition/assertion order, 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.
• Treat an unsynchronized static/global collection as both order-coupled and parallel-unsafe when tests read and write it. Also flag dereferencing a nullable result before the assertion intended to prove it non-null.
• 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).
Weak transformation oracle	A normalization, casing, trimming, mapping, or conversion test supplies an input already in the expected form, so a no-op implementation passes even though the assertion may catch other defects. Use an input that must change and assert an independently derived expected value. A producer/consumer round trip is useful but does not replace an independent format assertion when both sides could share the same defect.

Medium -- Maintainability and clarity issues

Anti-Pattern	What to Look For
Poor naming	Test names like Test1, TestMethod, or test that don't describe the scenario or outcome. Use the loaded extension when available; otherwise follow the existing naming convention in the same suite.
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. Shared mutable state is High when it is a latent isolation risk, but Critical when the user reports actual order-dependent failures or the code proves one test requires another to run first. 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 latent reliability risks to Warning and maintenance/cosmetic concerns to Info. Keep a demonstrated false-confidence or current order-dependency root cause Critical; do not downgrade it merely to make every requested tier non-empty. 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, not a self-comparison; still recommend one independent representation when producer and consumer could share a defect.
  • A transformation tested only with an already-transformed input. Keep it out of the tautology count, but report the weak oracle when removing the transformation would still pass. Use an input that must change and pin its independently expected output.
  • 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. Conversely, when a transformation test uses an already-normalized input, call out that the input cannot distinguish the real transformation from a no-op and provide a changing input plus exact expected output. For paired producer/consumer APIs, retain the round-trip test and add one independent representation oracle rather than replacing valid metamorphic evidence.
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