claudetools/.claude/agents/CODE_REVIEW_ST_TESTING.md

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Code Review Sequential Thinking Testing	Test scenarios for Code Review Agent with Sequential Thinking MCP

Code Review Agent - Sequential Thinking Testing

This document demonstrates the enhanced Code Review Agent with Sequential Thinking MCP integration.

Last Updated: 2026-01-17

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Test Scenarios

Scenario 1: Simple Issue (No ST Required)

Code Submitted:

def calculate_tax(amount):
    return amount * 0.08

Issues:

• Missing type hints
• No docstring
• Magic number (0.08)

Expected Behavior: Code reviewer fixes directly, approves ST Triggered: NO (simple, first review)

Review Result:

def calculate_tax(amount: Decimal) -> Decimal:
    """Calculate sales tax on given amount.

    Args:
        amount: Pre-tax amount

    Returns:
        Tax amount (8% rate)
    """
    TAX_RATE = Decimal("0.08")
    return amount * TAX_RATE

Status: APPROVED (after minor fixes)

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Scenario 2: First Rejection (Standard Escalation)

Code Submitted:

def login(username, password):
    user = db.query(f"SELECT * FROM users WHERE name='{username}'")
    if user and user.password == password:
        return True
    return False

Issues:

• SQL injection vulnerability
• Plaintext password comparison

Expected Behavior: Standard escalation format ST Triggered: NO (only 1 critical issue per category, first review)

Review Result:

## Code Review - Requires Revision

**Specification Compliance:** FAIL
**Security:** CRITICAL ISSUES

### CRITICAL: SQL Injection
- **Location:** login function, line 2
- **Problem:** String interpolation in SQL query
- **Impact:** Database compromise possible
- **Required Fix:** Use parameterized queries

### CRITICAL: Plaintext Password
- **Location:** login function, line 3
- **Problem:** Direct password comparison
- **Impact:** Passwords should be hashed (bcrypt)
- **Required Fix:** Hash passwords, use secure comparison

**Checklist:**
- [ ] Parameterized SQL queries
- [ ] Bcrypt password hashing

Status: ESCALATED (standard format)

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Scenario 3: Second Rejection (ST TRIGGERED)

Code Resubmitted After Fix:

def login(username, password):
    user = db.query("SELECT * FROM users WHERE name=?", username)
    if user and bcrypt.checkpw(password, user.password):
        session['user'] = username
        return True
    return False

Issues:

• Session management insecure (no token, no expiry)
• No rate limiting (brute force possible)
• Username might not exist (error handling)

Expected Behavior: ST TRIGGERED (2nd rejection) ST Analysis:

Thought 1: This is the second rejection. First was SQL injection + plaintext
passwords. Now session management issues. Pattern: treating authentication
as simple data retrieval rather than security-critical component.

Thought 2: Root cause is mental model problem. Coding Agent fixing surface
issues but missing the security-first mindset required for authentication.

Thought 3: Need comprehensive authentication pattern, not incremental fixes.

Thought 4: Should provide complete example with security threat model
explanation to shift mental model.

Thought 5: Include: parameterized queries (done), bcrypt (done), session
tokens (missing), rate limiting (missing), input validation (missing),
timing-safe comparison (missing).

Thought 6: Enhanced escalation format with educational context needed.

Review Result:

## Code Review - Requires Revision (Complex Issues Analyzed)

**Review Iteration:** 2 (USING SEQUENTIAL THINKING ANALYSIS)
**Reason:** 2nd rejection - pattern indicates mental model mismatch

## Root Cause Analysis

**Surface Issues:**
- Insecure session management
- No brute force protection
- Missing input validation

**Root Cause:**
Authentication is being treated as a CRUD operation (create/read user)
rather than a security-critical component with an active threat model.

**Why Previous Attempts Failed:**
Incremental security fixes without understanding the holistic security
requirements for authentication systems.

## Comprehensive Solution Strategy

**Recommended Approach:**
Implement complete authentication system with defense-in-depth:
- JWT tokens (not simple sessions)
- Rate limiting (prevent brute force)
- Input validation (prevent injection beyond SQL)
- Timing-safe comparison (prevent timing attacks)
- Audit logging (security monitoring)

**Why This Approach:**
- Addresses all OWASP authentication risks
- Provides defense-in-depth
- Industry standard pattern

**Complete Example:**
```python
from datetime import datetime, timedelta
import jwt
import bcrypt
from functools import wraps
from typing import Optional

# Rate limiting decorator
def rate_limit(max_attempts=5, window_minutes=15):
    attempts = {}
    def decorator(func):
        @wraps(func)
        def wrapper(username, *args, **kwargs):
            now = datetime.now()
            if username in attempts:
                count, first = attempts[username]
                if (now - first) < timedelta(minutes=window_minutes):
                    if count >= max_attempts:
                        raise Exception("Rate limit exceeded")
                    attempts[username] = (count + 1, first)
                else:
                    attempts[username] = (1, now)
            else:
                attempts[username] = (1, now)
            return func(username, *args, **kwargs)
        return wrapper
    return decorator

@rate_limit(max_attempts=5, window_minutes=15)
def login(username: str, password: str) -> Optional[str]:
    """Authenticate user and return JWT token.

    Security measures:
    - Parameterized queries (SQL injection prevention)
    - Bcrypt password hashing (credential theft protection)
    - Rate limiting (brute force prevention)
    - Input validation (injection prevention)
    - JWT tokens (secure session management)
    - Timing-safe comparison (timing attack prevention)

    Args:
        username: User's username (validated)
        password: User's plaintext password

    Returns:
        JWT token string if authenticated, None otherwise

    Raises:
        ValueError: If inputs are invalid
        Exception: If rate limit exceeded
    """
    # Input validation
    if not username or not isinstance(username, str):
        raise ValueError("Invalid username")
    if len(username) > 50:
        raise ValueError("Username too long")
    if not password or len(password) < 8:
        raise ValueError("Invalid password")

    # Parameterized query (SQL injection prevention)
    user = db.query(
        "SELECT id, username, password_hash FROM users WHERE username = ?",
        params=(username,)
    )

    # Timing-safe comparison to prevent timing attacks
    if not user:
        # Run bcrypt anyway to prevent timing leak
        bcrypt.checkpw(b"dummy", bcrypt.hashpw(b"dummy", bcrypt.gensalt()))
        return None

    # Bcrypt comparison (secure password verification)
    if not bcrypt.checkpw(password.encode(), user.password_hash):
        return None

    # Generate JWT token (secure session management)
    token = jwt.encode({
        'user_id': user.id,
        'username': user.username,
        'exp': datetime.utcnow() + timedelta(hours=24),
        'iat': datetime.utcnow()
    }, SECRET_KEY, algorithm='HS256')

    # Audit logging (security monitoring)
    audit_log.info(f"User {username} logged in", extra={
        'user_id': user.id,
        'ip': request.remote_addr,
        'timestamp': datetime.utcnow()
    })

    return token

Educational Context

Key Concept: Defense in Depth Authentication is an attack surface. Every decision has security implications. Multiple layers of defense ensure that if one fails, others protect the system.

Security Threat Model for Authentication:

1. SQL Injection → Parameterized queries
2. Credential theft → Password hashing
3. Brute force → Rate limiting
4. Session hijacking → JWT with expiry
5. Timing attacks → Constant-time comparison
6. Injection attacks → Input validation
7. Forensics → Audit logging

Why It Matters: Authentication failures lead to complete system compromise. A single vulnerability can bypass all other security measures.

Reference Patterns:

• OWASP Authentication Cheatsheet
• JWT Best Practices
• Bcrypt/Argon2 for password hashing

**Status:** ESCALATED (enhanced format with ST analysis)

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### Scenario 4: Complex Multiple Issues (ST TRIGGERED)

**Code Submitted:**
```python
def process_orders(user_id):
    orders = []
    user = db.query(f"SELECT * FROM users WHERE id={user_id}")
    for order in db.query("SELECT * FROM orders"):
        if order.user_id == user_id:
            items = []
            for item in db.query("SELECT * FROM items"):
                if item.order_id == order.id:
                    items.append(item)
            orders.append({'order': order, 'items': items})
    return orders

Issues:

• SQL injection (user_id)
• N+1 query problem (orders loop)
• N+1 query problem (items loop)
• Missing indexes implied
• Inefficient filtering in Python
• No error handling
• No type hints

Expected Behavior: ST TRIGGERED (3+ critical issues, interrelated) ST Analysis:

Thought 1: Multiple issues: SQL injection + N+1 queries (2 levels) +
inefficient filtering. These are interrelated - fixing one affects others.

Thought 2: Root cause: fundamental misunderstanding of database query
optimization. Treating database like in-memory collection.

Thought 3: Can't fix incrementally. Need to teach JOIN queries and
query optimization principles.

Thought 4: If I just say "use JOIN", they might not understand why or
how to structure it properly.

Thought 5: Need to show the performance impact (N+1 = 1000 orders * 100 items
= 100,000 queries vs 1 JOIN query), explain query planning, and provide
complete rewrite.

Thought 6: Also opportunity to teach about ORM vs raw SQL trade-offs.

Review Result: [Enhanced escalation format with performance analysis, query optimization education, and complete JOIN example]

Status: ESCALATED (enhanced format with ST analysis)

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Testing Checklist

When testing the enhanced code reviewer:

• Test simple issues (no ST, direct fix)
• Test first rejection (standard escalation)
• Test second rejection (ST triggered, enhanced format)
• Test 3+ critical issues (ST triggered, complexity)
• Test architectural issues (ST for trade-off analysis)
• Verify enhanced format includes root cause analysis
• Verify comprehensive examples in feedback
• Verify educational context in complex cases

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Expected Behavior Summary

Scenario	Rejection Count	Issue Complexity	ST Triggered?	Format Used
Simple formatting	0	Low	NO	Direct fix
First security issue	0	Medium	NO	Standard escalation
Second rejection	2	Medium	YES	Enhanced escalation
3+ critical issues	0-1	High	YES	Enhanced escalation
Architectural trade-offs	0-1	High	YES	Enhanced escalation
Complex interrelated	0-1	Very High	YES	Enhanced escalation

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Success Metrics

Enhanced code reviewer should:

1. Reduce rejection cycles - ST analysis breaks patterns faster
2. Provide better education - Comprehensive examples teach patterns
3. Identify root causes - Not just symptoms
4. Make better architectural decisions - Trade-off analysis with ST
5. Save tokens overall - Fewer rejections = less total token usage

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Last Updated: 2026-01-17 Status: Ready for Testing