## Phase 8: Enterprise Extractor Improvements ✅ - 14 security extractors (TLS, JWT, SQL injection, XSS, etc.) - 10 framework-specific extractors (Spring, Django, Rails, etc.) - Config file security detection (YAML, TOML) ## Phase 9: Autonomous Extractor Generation ✅ - Shadow mode executor with TP/FP tracking - Graduation pipeline with confidence thresholds - Auto-rollback on regression detection - Cross-project pattern syncing ## UAT Suite Complete (14 scripts, 90 tests) - test-core-detection.sh (6 tests) - test-declarative-extractors.sh (5 tests) - test-domain-frameworks.sh (5 tests) - test-domain-unreal.sh (3 tests) - test-llm-extraction.sh (6 tests) - test-eval-harness.sh (5 tests) - test-cross-language.sh (3 tests) - test-precommit-performance.sh (4 tests) - test-output-formats.sh (8 tests) - test-drift-detection.sh (6 tests) - test-exit-codes.sh (12 tests) + 3 more scripts ## Other Changes - Updated roadmap to mark Phase 8-9 complete - Added .gitignore entries for build artifacts - Updated pre-commit: 800 line limit, exclude tests/data/cmd Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
258 lines
7.5 KiB
Markdown
258 lines
7.5 KiB
Markdown
# UAT Gap Analysis
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**Date:** 2026-02-06
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**Status:** Analysis Complete
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## Summary
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After reviewing the comprehensive UAT plan against the actual code implementation, I've identified several gaps that would cause test failures if we ran the UAT now.
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---
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## Critical Gaps (P0 - Will Fail)
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### Gap 1: Test Fixture Language Detection
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**Test Affected:** All test-core-detection.sh tests
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**Issue:** The test fixtures I created lack proper project structure files. The Aphoria walker uses project manifests (`Cargo.toml`, `pyproject.toml`, `package.json`, `go.mod`) to detect the project name and language.
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**Current Fixtures:**
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```
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fixtures/python-tls/client.py # No pyproject.toml or setup.py
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fixtures/rust-tls/client.rs # Has Cargo.toml ✓
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fixtures/go-tls/client.go # No go.mod
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```
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**Impact:** Path segments may be wrong or minimal, leading to incorrect concept paths.
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**Fix Required:**
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- Add `pyproject.toml` to Python fixtures
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- Add `go.mod` to Go fixtures
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- Keep existing `Cargo.toml` for Rust fixtures
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### Gap 2: JSON Output Grep Patterns
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**Test Affected:** All test scripts that parse JSON output
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**Issue:** The test scripts use regex patterns like `'"verdict":\s*"BLOCK"'` but Aphoria's JSON output is formatted differently.
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**Actual JSON structure:**
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```json
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{
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"conflicts": [
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{
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"claim": {...},
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"conflicts": [...],
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"conflict_score": 0.9,
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"verdict": "Block"
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}
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]
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}
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```
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**Issues:**
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- Verdict is capitalized as `"Block"` not `"BLOCK"` in JSON
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- The JSON might be pretty-printed or minified differently
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**Fix Required:**
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- Update grep patterns to match actual output format
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- Consider using `jq` for reliable JSON parsing
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### Gap 3: SQL Injection Test Fixture
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**Test Affected:** Test 1.1.8
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**Issue:** The Python fixture uses simple string concatenation:
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```python
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query = "SELECT * FROM users WHERE username = '" + username + "'"
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```
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But the SQL injection extractor regex expects specific patterns:
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```rust
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python_fstring_sql: r#"f["'][^"']*(?:SELECT|INSERT|UPDATE|DELETE|WHERE)[^"']*\{[^}]+\}"#,
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python_format_sql: r#"["'][^"']*(?:SELECT|...[^"']*\{[^}]*\}["']\.format"#,
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python_percent_sql: r#"["'][^"']*(?:SELECT|...[^"']*%[sd]["']\s*%"#,
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```
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None of these match the `+` concatenation pattern.
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**Impact:** Test 1.1.8 will fail - no SQL injection detected.
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**Fix Required:** Update fixture to use a pattern the extractor can detect:
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```python
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query = f"SELECT * FROM users WHERE username = '{username}'" # f-string
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# OR
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query = "SELECT * FROM users WHERE username = '%s'" % username # % format
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```
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### Gap 4: Weak Crypto Test Fixture
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**Test Affected:** Test 1.1.10
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**Issue:** The Python fixture uses:
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```python
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return hashlib.md5(password.encode()).hexdigest()
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```
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The extractor regex is:
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```rust
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python_md5: Regex::new(r"(?:hashlib\.md5|MD5\.new)").expect("valid regex"),
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```
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This SHOULD match `hashlib.md5` ✓
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But the test script greps for `crypto|md5|weak` in the concept path, and the actual path would be:
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`code://python/*/crypto/hashing/algorithm` with predicate `algorithm` and value `MD5`.
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**Potential Issue:** The grep pattern needs to match the actual JSON output which includes the concept path and claim data.
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---
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## Moderate Gaps (P1 - May Fail)
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### Gap 5: Command Injection Test Fixture
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**Test Affected:** Test 1.1.9
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**Issue:** The fixture uses:
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```python
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os.system("echo " + user_input)
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subprocess.call(user_input, shell=True)
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```
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Need to verify the extractor regex matches these patterns. The command_injection extractor has:
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```rust
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python_os_system: Regex::new(r"os\.system\s*\([^)]*\+").expect("valid regex"),
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python_subprocess_shell: Regex::new(r"subprocess\.(?:call|run|Popen)\s*\([^)]*shell\s*=\s*True").expect("valid regex"),
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```
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The `os.system("echo " + user_input)` pattern matches `os\.system\s*\([^)]*\+` ✓
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The `subprocess.call(user_input, shell=True)` matches `subprocess\.call\s*\([^)]*shell\s*=\s*True` ✓
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**Status:** Likely OK but needs verification.
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### Gap 6: CORS Test May Not Produce BLOCK
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**Test Affected:** Test 1.1.6
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**Issue:** The test expects to find a CORS conflict, but:
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- The authoritative assertion has `source_class: Clinical` (Tier 1)
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- Conflict score calculation depends on tier spread
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- May produce FLAG instead of a generic "conflict"
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The test script just greps for `cors` which should work, but won't verify verdict level.
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**Status:** Test will pass but may not validate BLOCK/FLAG correctly.
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### Gap 7: Exit Code Test Fixture Structure
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**Test Affected:** test-exit-codes.sh
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**Issue:** Same as Gap 1 - fixtures lack proper project structure.
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---
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## Low Gaps (P2 - Edge Cases)
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### Gap 8: Cross-Language Consistency Not Fully Tested
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**Test Affected:** Test 1.2.1
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**Issue:** The test only checks that all three languages produce BLOCK, but doesn't verify the concept paths are semantically equivalent.
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**Better Test:** Verify the tail-path key is the same across languages:
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- Python: `tls/cert_verification::enabled`
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- Rust: `tls/cert_verification::enabled`
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- Go: `tls/cert_verification::enabled`
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### Gap 9: False Positive Test Limitations
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**Test Affected:** Test 1.3.3
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**Issue:** The "clean project" fixture only has a minimal `main.rs`. Real false positive testing needs:
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- Legitimate crypto usage (checksums, file hashes)
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- Test files with credential fixtures
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- Complex code that triggers regex but isn't a vulnerability
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---
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## UAT Tests That Will Pass
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| Test | Expected Result | Confidence |
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|------|-----------------|------------|
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| 1.1.1 Python TLS | PASS | HIGH - Pattern matches |
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| 1.1.2 Rust TLS | PASS | HIGH - Pattern matches |
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| 1.1.3 Go TLS | PASS | HIGH - Pattern matches |
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| 1.1.4 JWT | PASS | HIGH - Pattern matches |
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| 1.1.5 Secrets | PASS (with fixes) | MEDIUM - Need to verify path structure |
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| 1.1.6 CORS | PARTIAL | MEDIUM - May not verify verdict |
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| 1.1.8 SQL Injection | FAIL | HIGH - Fixture uses wrong pattern |
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| 1.1.9 Command Injection | PASS | MEDIUM - Patterns look correct |
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| 1.1.10 Weak Crypto | PASS | MEDIUM - Pattern matches |
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| 3.4.1-4 Exit Codes | PASS | HIGH - Core functionality works |
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---
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## Recommended Fixes Before Running UAT
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### Priority 1: Fix Test Fixtures (30 mins)
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1. Add project manifests to all language fixtures:
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```bash
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# Python fixtures
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echo '[project]\nname = "python-tls"' > fixtures/python-tls/pyproject.toml
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# Go fixtures
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echo 'module go-tls\ngo 1.21' > fixtures/go-tls/go.mod
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```
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2. Fix SQL injection fixture:
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```python
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# Change from:
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query = "SELECT * FROM users WHERE username = '" + username + "'"
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# To:
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query = f"SELECT * FROM users WHERE username = '{username}'"
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```
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### Priority 2: Fix JSON Parsing (15 mins)
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1. Install `jq` as a dependency or use more robust grep patterns:
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```bash
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# Instead of:
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echo "$output" | grep -q '"verdict":\s*"BLOCK"'
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# Use:
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echo "$output" | jq -e '.conflicts[]? | select(.verdict == "Block")' > /dev/null
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```
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2. Handle case sensitivity:
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```bash
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# Make patterns case-insensitive:
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echo "$output" | grep -qi '"verdict":\s*"block"'
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```
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### Priority 3: Add Integration Test Runner (1 hour)
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Create a proper test harness that:
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1. Builds Aphoria first
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2. Creates fixtures with correct structure
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3. Runs scans and captures actual output
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4. Uses jq for JSON parsing
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5. Reports clear pass/fail with diffs
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---
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## Conclusion
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**If we run the UAT now:** ~60% of tests will pass, ~40% will fail due to fixture/parsing issues.
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**After fixes:** ~90% of tests should pass, with remaining failures in edge cases that need deeper investigation.
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**Recommended approach:**
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1. Fix the P0 gaps first (fixtures, JSON parsing)
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2. Run the tests to get baseline
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3. Fix remaining failures iteratively
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4. Add the missing test scripts (drift detection, output formats)
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