Test Gap Analysis via Pseudo-Mutation

Analyze production code in any supported language by reasoning about hypothetical mutations and checking whether existing tests would catch them. 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.

This skill performs static pseudo-mutation — reasoning about mutations without actually running them — to approximate mutation testing at the speed of code review.

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
• 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 new tests 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

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

Boolean and Logic Mutations

Return Value Mutations

Exception / Error Removal Mutations

Arithmetic Mutations

Null / None / Nil-Check Removal Mutations

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:

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.

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)

Validation

• 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