Quality Checklist for AI-Generated Code

Introduction

Code generated by AI tools like DevLoop Runner is typically syntactically correct and passes tests. But "tests pass" does not automatically mean "high quality."

AI-generated code carries quality risks that differ from human-written code. Over-engineering, missing project context, and pattern inconsistencies are issues that require systematic checking.

This article provides a practical checklist for evaluating AI-generated code across five quality dimensions. While the AI PR review guide focuses on review mindset and strategies, this article provides the concrete items to verify.

Five Dimensions of Quality

Quality checks for AI-generated code fall into five categories:

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Let's look at the specific checklist items for each dimension.

1. Correctness Checklist

Correctness is the most critical quality dimension. Verify that the code fulfills the actual requirements.

Core Checks

• All requirements from the Issue are implemented
• No unnecessary features have been added (over-implementation check)
• Business logic branching matches the requirements
• Calculations and numeric processing are accurate (rounding, currency, etc.)
• Data types and formats match the specification

Test-Related Checks

• Test cases cover the key scenarios from the requirements
• Happy path tests are comprehensive
• Error case tests exist
• Boundary value tests are included
• Expected test values match the requirements

AI-Specific Pitfalls

AI interprets Issue descriptions and implements accordingly. Watch for these patterns:

Over-implementation: AI sometimes expands on requirements and adds features that were not requested.

Issue: "Add username validation"

AI implementation:
OK  Username length check (matches requirement)
OK  Prohibited character check (matches requirement)
NG  Email format validation (not in Issue)
NG  Password strength check (not in Issue)

Over-implementation may seem helpful but increases test scope and maintenance burden. Consider removing anything not specified in the Issue.

Missing context: AI may not know project-specific implicit rules.

Examples:
- Team convention: "delete" always means soft delete, but AI implemented hard delete
- Team rule: always use Decimal for monetary amounts, but AI used Float
- API convention: all responses use snake_case, but AI used camelCase

2. Readability Checklist

Readable code is easier to understand, debug, and maintain.

Naming Conventions

• Variable and function names follow project naming conventions
• Names clearly convey purpose (meaningful names, not abbreviations)
• Abbreviation usage matches project conventions
• Constants use the expected casing (UPPER_SNAKE_CASE, etc.)

Code Structure

• Functions and methods are appropriately sized (not too long)
• Nesting depth is reasonable (3 levels or fewer is a good target)
• Each function has a single responsibility
• Comments are present where logic is not self-evident
• No unnecessary comments on self-explanatory code

AI-Specific Pitfalls

AI sometimes writes code that is correct but hard to read.

# AI tendency (over-abstracted)
def process_items(items, processor_factory, validator_chain, config):
    pipeline = ProcessingPipeline(
        processors=[processor_factory.create(c) for c in config.stages],
        validators=validator_chain.build(),
        error_handler=config.error_strategy.create_handler()
    )
    return pipeline.execute(items)

# Project-appropriate (simple and direct)
def process_orders(orders):
    validated = [validate_order(order) for order in orders]
    return [calculate_total(order) for order in validated]

AI tends to favor high-abstraction patterns. Verify that the level of abstraction fits the project's scale and needs.

3. Maintainability Checklist

Maintainability determines how easily future changes and extensions can be made.

Architecture

• Follows existing architectural patterns
• Reuses existing utilities and shared functions (no duplicate implementations)
• Module dependencies are appropriate (no circular references)
• Proper layer separation (controllers, services, repositories, etc.)

Error Handling

• Error handling patterns match project conventions
• Error messages are informative and useful for debugging
• Exception types are used appropriately
• Resources are properly cleaned up on errors

Dependencies

• No unnecessary new packages added (minimum viable dependencies)
• Compatible with existing dependencies
• No license issues

AI-Specific Pitfalls

Pattern inconsistency: AI cannot fully grasp all patterns across an entire project.

Examples:
- Project uses Repository pattern, but AI wrote direct DB access in a Service
- Project uses Redux, but AI managed state with useState only in a new screen
- Project uses Winston for logging, but AI used console.log

These inconsistencies can be prevented by specifying "follow the existing [pattern name] convention" in the Issue.

4. Security Checklist

Security issues become exponentially more expensive to fix the later they are discovered. Check thoroughly during review.

Input Handling

• User input is sanitized
• SQL injection protection in place (parameterized queries)
• XSS protection in place (output escaping)
• Path traversal prevention
• File upload type and size restrictions

Authentication and Authorization

• Protected endpoints require authentication
• Authorization (permission checks) properly implemented
• Token expiration checks in place
• CSRF protection implemented

Data Protection

• Sensitive data not logged
• No hardcoded passwords or API keys
• Personal data handled appropriately
• HTTPS enforced

AI-Specific Pitfalls

AI follows general security best practices but may miss project-specific requirements.

Examples:
- Company policy requires loading API keys from environment variables,
  but AI wrote default values in a config file
- Certain endpoints should only be accessible from within the VPN,
  but AI did not implement IP restrictions

5. Performance Checklist

Performance issues often surface only in production, making proactive checks essential.

Database

• No N+1 queries
• Appropriate indexes designed
• Pagination used for large data sets
• No unnecessary columns fetched (avoid SELECT *)

Memory and Resources

• No memory leak patterns
• Large data sets not loaded entirely into memory
• File handles and connections properly closed
• Caching strategy is appropriate

Network

• API call frequency optimized (consider batching)
• Response size appropriate (returning only needed data)
• Timeouts configured

AI-Specific Pitfalls

AI prioritizes "working" code, which may not be optimally performant.

# AI tendency (inefficient)
for user in User.objects.all():
    orders = Order.objects.filter(user=user)  # N+1 query
    total = sum(order.amount for order in orders)

# Improved
users_with_totals = User.objects.annotate(
    total=Sum('order__amount')
)

Using the Checklist

PR Review Workflow

Here is how to integrate this checklist into your PR review process:

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Priority Order

You do not need to check every item every time. Follow this priority order:

Priority	Dimension	Rationale
Highest	Correctness	Other qualities are meaningless if requirements are not met
High	Security	Expensive to fix after the fact
Medium	Maintainability	Impacts long-term development velocity
Medium	Readability	Affects team-wide productivity
Lower	Performance	Often addressable when issues actually manifest

Team Adoption

Share this checklist across your team to establish consistent review standards.

• Embed in PR templates - Include the checklist in your GitHub PR template so reviewers never forget key items
• Use for onboarding - Having explicit review criteria helps junior members participate in reviews sooner
• Update regularly - Refine check items as your project evolves

For PR template design tips, see the effective PR description guide.

Copy-Ready Checklist

Here is a compact version you can paste directly into PR reviews:

## AI Code Quality Checklist

### Correctness
- [ ] All requirements implemented
- [ ] No over-implementation
- [ ] Business logic is accurate
- [ ] Test coverage is sufficient

### Security
- [ ] Input sanitization
- [ ] Auth checks in place
- [ ] Sensitive data protected
- [ ] No known vulnerability patterns

### Maintainability
- [ ] Consistent with existing patterns
- [ ] No duplicate implementations
- [ ] Error handling is appropriate
- [ ] Minimal dependencies

### Readability
- [ ] Naming conventions followed
- [ ] Code structure is clean
- [ ] Necessary comments present

### Performance
- [ ] No N+1 queries
- [ ] Memory usage is appropriate
- [ ] API calls optimized

Conclusion

• Evaluate AI-generated code across five dimensions: correctness, readability, maintainability, security, and performance
• Prioritize correctness and security first, then maintainability and readability
• Watch for AI-specific pitfalls: over-implementation, missing context, and pattern inconsistency
• Embed the checklist in PR templates to standardize review quality across your team
• Use this alongside the AI PR review guide to cover both the mindset and the mechanics of effective review

No matter how capable the AI, final quality assurance remains a human responsibility. Use this checklist to make your reviews both efficient and thorough.