test-gap-analysis

name	test-gap-analysis
description	Find or close verified gaps in existing tests. USE FOR: "add missing edge cases", "would tests catch this bug?", weak tests, survived boundaries/guards/ errors, or pseudo-mutation analysis. Polyglot. DO NOT USE FOR: a new suite (code-testing-agent), smells/assertion audits, or an actual mutation tool.
license	MIT

Test Gap Analysis via Pseudo-Mutation

Analyze production code in any supported language by reasoning about hypothetical mutations, then confirming them against the real test suite. This reveals blind spots where tests pass but would continue to pass even if the code were broken.

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). The extension file helps you find test files, recognize framework-specific assertion APIs, and identify language-specific null/None/nil patterns and error-handling idioms that map to the mutation catalog below.

Why Pseudo-Mutation Matters

Code coverage tells you what code ran during tests. It does not tell you whether tests would fail if that code were wrong. A method can have 100% line coverage but zero tests that would catch a sign flip, an off-by-one error, or a removed null check.

Pseudo-mutation analysis asks: "If I changed this line, would any test fail?" When the answer is "no," you've found a test gap.

Coverage Metric	What It Measures	What It Misses
Line coverage	Which lines executed	Whether assertions verify those lines' behavior
Branch coverage	Which branches taken	Whether both branches produce different asserted outcomes
Mutation score	Whether tests detect code changes	Nothing — this is the gold standard

This skill uses static pseudo-mutation to find mutation candidates at the speed of code review, then confirms each reported survivor by actually applying it and re-running the covering tests (Step 4b). Reasoning finds the candidates; execution decides the verdict.

When to Use

• User asks "would my tests catch a bug in this code?"
• User wants to find weak or shallow tests
• User wants to evaluate test effectiveness beyond coverage
• User asks for mutation testing or mutation analysis
• User asks "where are my tests blind?"
• User wants to prioritize which tests to strengthen
• User wants the verified gaps fixed with focused additions to an existing suite
• The code-testing-generator agent (or any test-generation workflow) calls this skill as a pre-completion self-review step on freshly generated tests, before declaring the run finished

When Not to Use

• User wants to write a new suite from scratch (use code-testing-agent for any language, or writing-mstest-tests for MSTest specifically)
• User wants to detect test anti-patterns like flakiness or poor naming (use test-anti-patterns)
• User wants to measure assertion variety (use assertion-quality)
• User wants to run an actual mutation testing framework (Stryker for .NET/JS/TS, mutmut for Python, PIT for Java, go-mutesting for Go, cargo-mutants for Rust, mutant for Ruby) — help them directly with the tool
• User only wants code coverage numbers (out of scope)

Inputs

Input	Required	Description
Production code	Yes	The source files to analyze for mutation points
Test code	Yes	The test files that cover the production code
Focus area	No	A specific mutation category or code region to focus on

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 mutation catalog below uses language-neutral concepts; the extension file tells you how each concept maps in the language you are analyzing (e.g., null vs None vs nil vs undefined, throw vs raise vs panic! vs return err).

Step 2: Gather production and test code

Read both the production code and its corresponding test files. If the user points to a directory, identify production/test pairs by convention — defaults differ by language: .cs ↔ *Tests.cs/*.Tests.cs (.NET), foo.py ↔ test_foo.py/foo_test.py (Python), foo.ts ↔ foo.test.ts/foo.spec.ts (JS/TS), Foo.java ↔ FooTest.java/FooTests.java (Java), foo.go ↔ foo_test.go (Go), foo.rb ↔ foo_spec.rb/test_foo.rb (Ruby), lib.rs ↔ inline #[cfg(test)] mod tests or tests/foo.rs (Rust), Foo.swift ↔ FooTests.swift (Swift), Foo.kt ↔ FooTest.kt/FooSpec.kt (Kotlin), Foo.ps1 ↔ Foo.Tests.ps1 (Pester), foo.cpp ↔ foo_test.cpp/test_foo.cpp (C++).

Establish which production methods are exercised by which test methods — trace this through method calls in test code, setup, helper methods, and shared examples.

Step 3: Identify mutation points

Scan the production code and annotate every location where a mutation could reveal a test gap. Use the mutation catalog below.

Boundary Mutations

Original	Mutation	What it tests
<	<=	Off-by-one at upper bound
>	>=	Off-by-one at lower bound
<=	<	Boundary inclusion
>=	>	Boundary inclusion
== 0	== 1 or <= 0	Zero-boundary handling
i < length	i < length - 1 or i <= length	Loop boundary
index + 1	index or index + 2	Index arithmetic

Boolean and Logic Mutations

Original	Mutation	What it tests
&&	||	Condition independence
||	&&	Condition necessity
!condition	condition	Negation correctness
if (x)	if (!x)	Branch selection
true (constant)	false	Hardcoded assumption
flag || other	other	Short-circuit first operand

Return Value Mutations

Original	Mutation	What it tests
return result	return null / return None / return nil / return undefined	Null/None/nil handling downstream
return result	return default(T) / return T() / return "" / return 0	Default value handling
return true	return false	Boolean return verification
return list	return new List<T>() / return [] / return Array.Empty<T>() / return make([]T, 0) / return Vec::new() / return @[]	Empty collection handling
return count	return 0 or return count + 1	Numeric return verification
return string	return "" or return null/None/nil	String return verification
return Ok(x)	return Err(...) (Rust)	Result/error variant
return value, nil	return zero, err (Go)	Error tuple

Exception / Error Removal Mutations

Original	Mutation	What it tests
throw new ArgumentNullException(...) (.NET) / raise ValueError(...) (Python) / throw new Error(...) (JS) / throw new IllegalArgumentException(...) (Java) / panic!(...) (Rust) / panic(...) (Go) / raise ArgumentError (Ruby) / throw RuntimeException(...) (Kotlin) / throw FooError.bar (Swift) / throw "..." (Pester) / throw std::invalid_argument(...) (C++)	(remove entire throw/raise/panic)	Guard clause verification
if (x == null) throw ... / if x is None: raise ... / if (!x) throw ... / if x == nil { return err } (Go) / assert!(x.is_some()) (Rust)	(remove entire guard)	Null/None/nil guard testing
if (!IsValid()) throw ... / if not is_valid(): raise ... / etc.	(remove entire check)	Validation testing
return err after error check (Go)	(remove or swallow error)	Error propagation
? operator (Rust)	.unwrap() or .expect(...)	Error short-circuit

Arithmetic Mutations

Original	Mutation	What it tests
a + b	a - b	Addition correctness
a - b	a + b	Subtraction correctness
a * b	a / b	Multiplication correctness
a / b	a * b	Division correctness
a % b	a / b	Modulo correctness
x++	x--	Increment direction
-value	value	Sign flip

Null / None / Nil-Check Removal Mutations

Original	Mutation	What it tests
if (x == null) return ... / if x is None: return ... / if (!x) return ... / if x == nil { return ... } / unless x; return; end (Ruby) / if x.is_none() { return ... } (Rust)	(remove null/None/nil check)	Null path coverage
if (x != null) { ... } / if x is not None: ... / if x: ... / if x != nil { ... } / x?.let { ... } (Kotlin) / if let Some(x) = ... { ... } (Rust)	(always enter block)	Null/None/nil guard necessity
x ?? defaultValue (.NET/JS/Swift) / x or defaultValue (Python) / x || defaultValue (JS) / x.unwrap_or(defaultValue) (Rust) / x || defaultValue (Kotlin: x ?: defaultValue)	x (drop coalescing)	Null coalescing coverage
x?.Method() (.NET/Swift/Kotlin) / x && x.method() (JS) / x and x.method() (Python)	x.Method()	Null-conditional coverage
x! (.NET/TS/Swift) / x!! (Kotlin) / .unwrap() (Rust)	x	Null-forgiving / unwrap necessity

Step 4: Evaluate each mutation against tests

For each identified mutation point, reason about whether existing tests would detect the change:

1. Find covering tests — Which test methods exercise the mutated line? Follow call chains through helpers and setup methods.
2. Check assertion relevance — Do those tests assert something that would change if the mutation were applied? A test that calls the method but only asserts an unrelated property would NOT catch the mutation.
3. Classify the mutation as:

Verdict	Meaning	Action
Killed	At least one test would fail if this mutation were applied	No action needed — tests are effective here
Survived	No test would fail — the mutation would go undetected	This is a test gap — recommend a test improvement
No coverage	No test exercises this code path at all	Worse than survived — the code is untested
Equivalent	The mutation produces identical behavior (e.g., x * 1 → x / 1)	Skip — not a real mutation

Step 4b: Verify every reported survivor by running the tests

Static reasoning alone produces false "survived" verdicts — the most common failure of this skill is telling a user their tests are weak when the tests actually catch the bug. If the suite can be built and run, verify before you report.

1. Establish a green baseline. Run the suite (dotnet test, pytest, npm test, go test ./..., cargo test, …). Record the pass count. If it does not build or run, fix only obvious wiring problems (missing project reference, wrong runner arguments) — never rewrite production behavior.
2. Apply each candidate survivor as a real edit, one at a time, to the production file.
3. Re-run the narrowest covering test(s). Still green ⇒ genuinely Survived. Now red ⇒ Killed; drop it from the findings.
4. Revert immediately after each check, and confirm the suite is green again before finishing. Never leave a mutation in the working tree.
5. Report what you did: state that survivors were empirically confirmed and give the observed kill count (e.g. "8 of 9 injected mutations were caught").

When the suite genuinely cannot be executed (no toolchain, no network, compile errors you must not fix), fall back to reasoning — but label every finding unverified (static reasoning), and downgrade its confidence rather than presenting it as a proven gap.

Step 5: Calibrate findings

Before reporting, apply these calibration rules:

• Don't flag trivial code. Simple property getters (return _name;), auto-properties, and boilerplate don't need mutation analysis. Focus on logic, conditions, calculations, and error handling.
• Consider defensive depth. If a null guard has a survived mutation but the caller also checks for null, note the redundancy but rate it lower priority.
• Equivalent mutations are not gaps. If changing >= to > doesn't alter behavior because the == case is impossible given the domain, mark it Equivalent and skip.
• Private methods reached through public API are valid targets. Trace through the call chain — a private method called from a tested public method may still have survived mutations if the test doesn't assert the specific behavior affected.
• Rate by risk, not count. A single survived mutation in payment calculation logic is more important than five survived mutations in logging code.
• When most mutations are killed, lead with that. A strong suite with one or two minor survivors is a good result: say "the tests are strong" first and present the survivors as minor improvements. Do not attach "critical" / "HIGH RISK" framing to a suite that kills the substantive mutations.
• Never claim a gap you did not verify. If Step 4b confirmed the mutation is caught, it is not a finding. Prefer under-claiming: a missed gap costs the user far less than a false alarm that sends them writing redundant tests.

Step 6: Report findings

Present the analysis in this structure:

1. Summary — Overall mutation score and key findings:

   | Metric              | Value    |
   |---------------------|----------|
   | Mutation points      | 42       |
   | Killed               | 28 (67%) |
   | Survived             | 10 (24%) |
   | No coverage          | 2 (5%)   |
   | Equivalent (skipped) | 2 (5%)   |
   

2. Survived Mutations (Test Gaps) — For each survived mutation, report:

   • Location: File, method, line
   • Mutation category: Boundary / Boolean / Return value / Exception / Arithmetic / Null-check
   • Original code: The current code
   • Hypothetical mutation: What would change
   • Why it survives: Which tests cover this code and why their assertions miss it
   • Recommended fix: A concrete test assertion or new test case that would kill this mutation

   Group by priority: high-risk survived mutations first (business logic, calculations, security checks), lower-risk last (logging, formatting).

3. No-Coverage Zones — Code paths that no test reaches at all. These are worse than survived mutations.

4. Killed Mutations (Strengths) — Briefly note areas where tests are effective. Highlight well-tested methods and strong assertion patterns. Don't enumerate every killed mutation — summarize.

5. Recommendations — Prioritized list:

   • Which survived mutations to address first (by risk)
   • Specific test methods to add or strengthen
   • Patterns the team can adopt to prevent future gaps (e.g., always test boundary values, always assert exception types)

Step 7: Close verified gaps when requested

When the user asks to add/fix tests rather than only report:

1. Implement tests only for empirically Survived or No coverage behavior. A plausible edge whose mutation was killed is not a gap; do not duplicate it.
2. Preserve existing test files when requested and follow the suite's framework and naming conventions.
3. Prefer one focused test that kills several related mutations over one test per syntax variation.
4. Run the narrow suite, re-apply each original mutation, and confirm the new tests kill it. Revert every mutation immediately.
5. Report the added test names and the mutations they now kill. Never leave a mutation or production-code change in the workspace.

Validation

• The suite was run and every reported survivor was empirically confirmed (or all findings are explicitly labelled unverified)
• All applied mutations were reverted and the suite is green again
• Every mutation point was classified (Killed / Survived / No coverage / Equivalent)
• Every survived mutation includes the original code, the hypothetical change, and why tests miss it
• Every survived mutation includes a concrete recommended fix (a test assertion or test case)
• Equivalent mutations are correctly identified and excluded from the score
• Trivial code (simple getters, auto-properties) is excluded from analysis
• Findings are prioritized by risk, not just listed in source order
• Report includes strengths (killed mutations) alongside gaps
• Mutation categories are correctly labeled

Common Pitfalls

Pitfall	Solution
Reporting survivors you never ran	Apply the mutation, re-run the covering tests, revert. An unverified "survived" claim that the tests actually kill is worse than no analysis
Overstating severity on a strong suite	If the substantive mutations are killed, open with "tests are strong" and frame the remainder as minor improvements
Publishing your reasoning as it changes	Settle the verdict first; never emit "wait, no… this is killed… let me recalibrate" in the report
Leaving a mutation applied	Revert every edit and re-run the suite before reporting
Analyzing trivial code	Skip auto-properties, simple getters, @dataclass/record/data class accessors, #[derive] impls — focus on logic
Reporting equivalent mutations as gaps	If the mutation doesn't change behavior, it's not a gap — mark Equivalent
Ignoring call chains	A private/internal/unexported helper called from a tested public method is reachable — trace the chain
Over-counting mutations in generated code	Skip auto-generated code (*.g.cs, *.designer.cs, *_pb.go, *.pb.dart), designer files, migration files, generated mocks/stubs
Recommending a new test for every survived mutation	Multiple survived mutations in the same method often share a single missing test — recommend one test that kills several
Ignoring production context	A survived mutation in ToString() / __repr__ / toString() formatting is less important than one in CalculateTotal() — prioritize by business risk
Claiming 100% kill rate is required	Some mutations in low-risk code are acceptable to leave — acknowledge this in the report
Not considering integration with other skills	If gaps are found, mention that code-testing-agent (any language) or writing-mstest-tests (MSTest-specific) can help write the missing tests, and test-anti-patterns can audit existing test quality
Forgetting Go's error idiom	Removing is a valid mutation target only when the function actually does something else with (e.g., wrap, log, branch). Bare passthroughs in idiomatic Go are not meaningful gaps.