white-box-red-testing

name: white-box-red-testing description: > Find bugs by writing tests that should pass but don't. Invoke manually on user-chosen scope (commits, files, or coverage threshold). Outputs red tests with structured rationale. Use when user asks to "stress-test", "find bugs in", "attack", or "break" code.

White-Box Red Testing

Hard Boundaries

• NEVER modify source code. Analyze and classify only.
• NEVER weaken existing tests.
• Unexported/private members: test only if explicitly scoped.
• Output to user-specified directory or language-appropriate default.

When to Use

• You have specific targets: changed files, a commit range, or low-coverage functions
• You need classified findings (confirmed-bug / likely-bug / specification-gap)
• Contracts exist (docstrings, types, tests) and you want to verify code matches them
• After a commit or PR — "did these changes introduce bugs?"

If you don't know where to look or want to explore broadly, use the black-box-red-testing skill instead.

Scope Modes

User chooses scope. Never run unsolicited.

Note on coverage targeting: Low coverage indicates under-tested code, not necessarily buggy code. Use as a targeting heuristic to prioritize where to look, not as a bug predictor.

Workflow

1. IDENTIFY TARGETS
   - commits: git diff → parse changed functions
   - files: AST parse → list public callables
   - coverage: run scripts/discover_targets.py

2. GATHER CONTRACT EVIDENCE per target
   - docstrings, type annotations, existing passing tests
   - function/param names, assertions, call sites, commit messages
   - No evidence? → findings become "specification-gap"

3. FORM HYPOTHESES per target as structured one-liners:
   [code_path] × [defect_class] → [observable_symptom]

   Defect classes to consider:
   - boundary inputs (empty, zero, None, unicode, tz-naive)
   - state/mutation (shared state, call sequences, input mutation)
   - implicit contracts (name promises vs actual behavior)
   - error paths (timeouts, missing data, malformed input)

4. GENERATE ADVERSARIAL TESTS — one test per hypothesis

5. SELF-VALIDATE (mandatory)
   - Run all generated tests
   - Red → candidate finding
   - Green → record hypothesis in confidence section of report
   - Broken → fix or discard

6. CLASSIFY per references/finding-classification.md
   - confirmed-bug | likely-bug | specification-gap

7. APPLY DISTINCTNESS FILTER
   Each finding must differ from others in at least one of:
   - The code path exercised
   - The category of defect found
   - The component boundary tested
   Shallow variations of the same finding → consolidate into one.

8. OUTPUT
   - Test files to output directory (test code only, no classification in tests)
   - Summary report to stdout (classification, evidence, impact per finding)
   - Format: see references/output-format.md

Constraints

• Coverage scope mode requires Python + pytest-cov. Commits and Files modes are language-agnostic.
• Coverage scope uses scripts/discover_targets.py — run with --help for options
• If >50 targets, ask user to narrow scope

Stop Conditions

• Max 5 generation attempts per target — if 3 attempts yield nothing, shift defect category (boundary → state → contract → error path) rather than retrying the same angle

• 50% of targets yield no findings → report as confidence signal for tested areas, continue with remaining targets

• 15 findings before all targets analyzed → pause for triage