stemedb/applications/aphoria/uat/gap-analysis-2026-02-06.md

UAT Gap Analysis

Date: 2026-02-06 Status: Analysis Complete

Summary

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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Critical Gaps (P0 - Will Fail)

Gap 1: Test Fixture Language Detection

Test Affected: All test-core-detection.sh tests

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.

Current Fixtures:

fixtures/python-tls/client.py  # No pyproject.toml or setup.py
fixtures/rust-tls/client.rs    # Has Cargo.toml ✓
fixtures/go-tls/client.go      # No go.mod

Impact: Path segments may be wrong or minimal, leading to incorrect concept paths.

Fix Required:

• Add pyproject.toml to Python fixtures
• Add go.mod to Go fixtures
• Keep existing Cargo.toml for Rust fixtures

Gap 2: JSON Output Grep Patterns

Test Affected: All test scripts that parse JSON output

Issue: The test scripts use regex patterns like '"verdict":\s*"BLOCK"' but Aphoria's JSON output is formatted differently.

Actual JSON structure:

{
  "conflicts": [
    {
      "claim": {...},
      "conflicts": [...],
      "conflict_score": 0.9,
      "verdict": "Block"
    }
  ]
}

Issues:

• Verdict is capitalized as "Block" not "BLOCK" in JSON
• The JSON might be pretty-printed or minified differently

Fix Required:

• Update grep patterns to match actual output format
• Consider using jq for reliable JSON parsing

Gap 3: SQL Injection Test Fixture

Test Affected: Test 1.1.8

Issue: The Python fixture uses simple string concatenation:

query = "SELECT * FROM users WHERE username = '" + username + "'"

But the SQL injection extractor regex expects specific patterns:

python_fstring_sql: r#"f["'][^"']*(?:SELECT|INSERT|UPDATE|DELETE|WHERE)[^"']*\{[^}]+\}"#,
python_format_sql: r#"["'][^"']*(?:SELECT|...[^"']*\{[^}]*\}["']\.format"#,
python_percent_sql: r#"["'][^"']*(?:SELECT|...[^"']*%[sd]["']\s*%"#,

None of these match the + concatenation pattern.

Impact: Test 1.1.8 will fail - no SQL injection detected.

Fix Required: Update fixture to use a pattern the extractor can detect:

query = f"SELECT * FROM users WHERE username = '{username}'"  # f-string
# OR
query = "SELECT * FROM users WHERE username = '%s'" % username  # % format

Gap 4: Weak Crypto Test Fixture

Test Affected: Test 1.1.10

Issue: The Python fixture uses:

return hashlib.md5(password.encode()).hexdigest()

The extractor regex is:

python_md5: Regex::new(r"(?:hashlib\.md5|MD5\.new)").expect("valid regex"),

This SHOULD match hashlib.md5 ✓

But the test script greps for crypto|md5|weak in the concept path, and the actual path would be: code://python/*/crypto/hashing/algorithm with predicate algorithm and value MD5.

Potential Issue: The grep pattern needs to match the actual JSON output which includes the concept path and claim data.

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Moderate Gaps (P1 - May Fail)

Gap 5: Command Injection Test Fixture

Test Affected: Test 1.1.9

Issue: The fixture uses:

os.system("echo " + user_input)
subprocess.call(user_input, shell=True)

Need to verify the extractor regex matches these patterns. The command_injection extractor has:

python_os_system: Regex::new(r"os\.system\s*\([^)]*\+").expect("valid regex"),
python_subprocess_shell: Regex::new(r"subprocess\.(?:call|run|Popen)\s*\([^)]*shell\s*=\s*True").expect("valid regex"),

The os.system("echo " + user_input) pattern matches os\.system\s*\([^)]*\+ ✓ The subprocess.call(user_input, shell=True) matches subprocess\.call\s*\([^)]*shell\s*=\s*True ✓

Status: Likely OK but needs verification.

Gap 6: CORS Test May Not Produce BLOCK

Test Affected: Test 1.1.6

Issue: The test expects to find a CORS conflict, but:

• The authoritative assertion has source_class: Clinical (Tier 1)
• Conflict score calculation depends on tier spread
• May produce FLAG instead of a generic "conflict"

The test script just greps for cors which should work, but won't verify verdict level.

Status: Test will pass but may not validate BLOCK/FLAG correctly.

Gap 7: Exit Code Test Fixture Structure

Test Affected: test-exit-codes.sh

Issue: Same as Gap 1 - fixtures lack proper project structure.

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Low Gaps (P2 - Edge Cases)

Gap 8: Cross-Language Consistency Not Fully Tested

Test Affected: Test 1.2.1

Issue: The test only checks that all three languages produce BLOCK, but doesn't verify the concept paths are semantically equivalent.

Better Test: Verify the tail-path key is the same across languages:

• Python: tls/cert_verification::enabled
• Rust: tls/cert_verification::enabled
• Go: tls/cert_verification::enabled

Gap 9: False Positive Test Limitations

Test Affected: Test 1.3.3

Issue: The "clean project" fixture only has a minimal main.rs. Real false positive testing needs:

• Legitimate crypto usage (checksums, file hashes)
• Test files with credential fixtures
• Complex code that triggers regex but isn't a vulnerability

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UAT Tests That Will Pass

Test	Expected Result	Confidence
1.1.1 Python TLS	PASS	HIGH - Pattern matches
1.1.2 Rust TLS	PASS	HIGH - Pattern matches
1.1.3 Go TLS	PASS	HIGH - Pattern matches
1.1.4 JWT	PASS	HIGH - Pattern matches
1.1.5 Secrets	PASS (with fixes)	MEDIUM - Need to verify path structure
1.1.6 CORS	PARTIAL	MEDIUM - May not verify verdict
1.1.8 SQL Injection	FAIL	HIGH - Fixture uses wrong pattern
1.1.9 Command Injection	PASS	MEDIUM - Patterns look correct
1.1.10 Weak Crypto	PASS	MEDIUM - Pattern matches
3.4.1-4 Exit Codes	PASS	HIGH - Core functionality works

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Recommended Fixes Before Running UAT

Priority 1: Fix Test Fixtures (30 mins)

1. Add project manifests to all language fixtures:

# Python fixtures
echo '[project]\nname = "python-tls"' > fixtures/python-tls/pyproject.toml

# Go fixtures
echo 'module go-tls\ngo 1.21' > fixtures/go-tls/go.mod

2. Fix SQL injection fixture:

# Change from:
query = "SELECT * FROM users WHERE username = '" + username + "'"

# To:
query = f"SELECT * FROM users WHERE username = '{username}'"

Priority 2: Fix JSON Parsing (15 mins)

1. Install jq as a dependency or use more robust grep patterns:

# Instead of:
echo "$output" | grep -q '"verdict":\s*"BLOCK"'

# Use:
echo "$output" | jq -e '.conflicts[]? | select(.verdict == "Block")' > /dev/null

2. Handle case sensitivity:

# Make patterns case-insensitive:
echo "$output" | grep -qi '"verdict":\s*"block"'

Priority 3: Add Integration Test Runner (1 hour)

Create a proper test harness that:

1. Builds Aphoria first
2. Creates fixtures with correct structure
3. Runs scans and captures actual output
4. Uses jq for JSON parsing
5. Reports clear pass/fail with diffs

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Conclusion

If we run the UAT now: ~60% of tests will pass, ~40% will fail due to fixture/parsing issues.

After fixes: ~90% of tests should pass, with remaining failures in edge cases that need deeper investigation.

Recommended approach:

1. Fix the P0 gaps first (fixtures, JSON parsing)
2. Run the tests to get baseline
3. Fix remaining failures iteratively
4. Add the missing test scripts (drift detection, output formats)