---

name: smith-validation description: Hypothesis testing, root cause analysis, and debugging techniques. Use when debugging, testing hypotheses, validating solutions, proving correctness, or performing root cause analysis on failures.

Verification Techniques

• Scope: Hypothesis testing, root cause analysis, and verification
• Load if: Bug reported, test failure, proving correctness, root cause analysis
• Prerequisites: @smith-guidance/SKILL.md

Foundation: Based on PDSA's Study phase (Deming) and Popper's Falsification - understanding WHY something works or doesn't, not just IF it works.

When to use: Debugging, testing hypotheses, validating solutions, proving correctness.

Hypothesis Testing

Strong Inference

Rapid progress through multiple competing hypotheses:

1. Devise multiple hypotheses - Not just one, but several alternatives
2. Design crucial experiments - Tests that exclude one or more hypotheses
3. Execute experiments - Run tests to eliminate hypotheses
4. Iterate - Refine remaining hypotheses, repeat

Key insight: Science advances fastest when we actively try to disprove hypotheses, not confirm them.

For debugging:

• Bug: "Login fails intermittently"
• H1: Session storage full
• H2: Race condition in token refresh
• H3: Network timeout on auth server
• Crucial test: Check if failures correlate with session count (tests H1)

Falsification Principle (Popper)

A theory is scientific only if it can be proven false:

• Design tests that could disprove your hypothesis
• Seek evidence that contradicts, not confirms
• One counterexample disproves a universal claim

Anti-pattern: Only running tests you expect to pass Good practice: Actively try to break your own code

Root Cause Analysis

5 Whys (Toyota)

Root cause analysis through iterative questioning:

1. State the problem
2. Ask "Why did this happen?"
3. Repeat for each answer (typically 5 times)
4. Stop when you reach an actionable root cause

Example:

• Bug: Users logged out unexpectedly
• Why? Session expired
• Why? Token refresh failed
• Why? Refresh endpoint returned 401
• Why? Clock skew between servers
• Root cause: NTP not configured on auth server

Caution: Don't stop at symptoms. "Why?" should reach systemic causes.

Explanation Techniques

Rubber Duck Debugging

Explain code line-by-line aloud; when explanation doesn't match code, you've found the bug.

For AI agents: When stuck, explain the problem step-by-step before proposing solutions.

Feynman Technique

Explain simply to reveal gaps: Choose concept → Explain to child → Identify gaps → Review.

If you can't explain it simply, you don't understand it well enough.

Systematic Isolation

Delta Debugging

Minimize failing input: split in half, test each, recurse on failing half until minimal.

Use when: Large input crashes, many files break tests, config changes fail.

Scientific Debugging (TRAFFIC)

Track → Reproduce → Automate → Find origins → Focus → Isolate → Correct

Work backward: Failure → Propagation → Infection → Defect.

Version Control Debugging

Git Bisect

Binary search through commit history:

Usage:

Copygit bisect start
git bisect bad
git bisect good abc1234
git bisect good
git bisect reset

Mark current as bad, known-good commit, then test each checkout (good/bad) until culprit found.

Automated:

Copygit bisect run ./test.sh

Exit codes: 0 = good, 1-127 = bad, 125 = skip

Complexity: O(log n) - tests ~7 commits for 100 commit range

When to use:

• Regression appeared, unknown when
• Automated test can detect the bug
• Need to find exact commit that broke something

Coverage-Based Localization

Spectrum-Based Fault Localization (SBFL)

Use test coverage data to locate bugs:

Concept: Statements executed by failing tests but not passing tests are more suspicious.

Ochiai Formula (most effective):

Copysuspiciousness(s) = failed(s) / sqrt(total_failed * (failed(s) + passed(s)))

Practical application:

1. Run test suite with coverage
2. Note which tests fail
3. Rank statements by how often they appear in failing vs passing tests
4. Inspect highest-ranked statements first

For AI agents: When multiple tests fail, identify code paths common to failures but not successes.

ACTION (Recency Zone)

When debugging or validating:

1. Use Strong Inference: devise multiple hypotheses before testing
2. Apply 5 Whys to find root cause, not symptoms
3. Use Git Bisect for regressions (binary search ~7 commits for 100-commit range)
4. Run tests with coverage; inspect code paths common to failures

Claude Code Plugin Integration

When pr-review-toolkit is available:

• silent-failure-hunter agent: Detects silent failures, inadequate error handling
• Analyzes catch blocks, fallback behavior, missing logging
• Trigger: "Check for silent failures" or use Task tool

Ralph Loop Integration

Debugging = Ralph iteration: hypothesis → test → eliminate → iterate until <promise>ROOT CAUSE FOUND</promise>.

See @smith-ralph/SKILL.md for full patterns.

• @smith-guidance/SKILL.md - Anti-sycophancy, HHH framework, exploration workflow
• @smith-analysis/SKILL.md - Reasoning patterns, problem decomposition
• @smith-clarity/SKILL.md - Cognitive guards, logic fallacies