Let AI Handle Refactoring - New Methods for Code Quality Improvement

Introduction

As codebases grow, technical debt becomes inevitable. Outdated patterns, duplicated code, inconsistent naming conventions, deeply nested logic -- these accumulate over time, slowing down feature development, introducing bugs, and making onboarding new team members harder than it should be.

Everyone understands the importance of refactoring. But in practice, "the risk of touching working code" and "the pressure of daily feature delivery" keep pushing refactoring down the priority list.

DevLoop Runner changes this equation. AI automatically detects technical debt, executes refactoring through Issue-based workflows, and submits PRs with tests included. You focus on reviewing the results, which dramatically lowers the barrier to keeping your codebase healthy.

This article covers the complete refactoring workflow with DevLoop Runner, from category-specific approaches to Issue sizing guidelines and test safety strategies.

The Refactoring Workflow with DevLoop Runner

Refactoring with DevLoop Runner follows three key steps.

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Step 1: Detecting Technical Debt

First, identify where refactoring is needed. DevLoop Runner's Create Issue feature lets AI analyze your codebase and automatically detect areas that need improvement.

Common types of detected issues:

Category	Examples
Code duplication	Similar logic scattered across multiple files
Design problems	God classes, excessive coupling
Naming inconsistencies	Mixed camelCase and snake_case, vague variable names
Performance	Unnecessary re-renders, N+1 queries
Technical staleness	Deprecated API usage, outdated patterns

Issues detected by Create Issue are automatically created as GitHub Issues, saving you the effort of writing them manually.

Step 2: Triage and Prioritization

Review the Issues generated by Create Issue and prioritize them. You don't need to tackle everything at once. Balance impact against effort to determine what to address first.

Prioritization criteria:

• High: Causing bugs or blocking new feature development
• Medium: Reducing readability and increasing maintenance time
• Low: Minor naming improvements or coding convention mismatches

Step 3: Execute with Dev Run

Once priorities are set, run Dev Run. For refactoring Issues, the following phases are particularly important:

• Planning Phase: Aoi (PM) understands the Issue intent and formulates the refactoring strategy
• Design Phase: Riku (Tech Lead) analyzes existing code and identifies the impact scope
• Test Scenario Phase: Sumire (QA) checks the impact on existing tests and designs new ones
• Implementation Phase: Riku executes the refactoring based on the design
• Testing Phase: Sumire runs all tests to verify existing functionality is preserved

This structured process minimizes the risk of "refactoring broke something else" -- one of the most common fears that prevents teams from refactoring.

Category-Specific Refactoring Approaches

Refactoring serves different purposes depending on the type of problem. Here are four major categories with Issue-writing tips and DevLoop Runner best practices.

1. Eliminating Code Duplication

When the same logic exists in multiple places, consolidating it into shared utilities dramatically improves maintainability.

Before:

// src/components/UserList.tsx
const formatDate = (date: Date) => {
  const y = date.getFullYear();
  const m = String(date.getMonth() + 1).padStart(2, "0");
  const d = String(date.getDate()).padStart(2, "0");
  return `${y}-${m}-${d}`;
};

// src/components/OrderHistory.tsx
const formatDate = (date: Date) => {
  const y = date.getFullYear();
  const m = String(date.getMonth() + 1).padStart(2, "0");
  const d = String(date.getDate()).padStart(2, "0");
  return `${y}-${m}-${d}`;
};

After:

// src/utils/dateFormat.ts
export const formatDate = (date: Date): string => {
  const y = date.getFullYear();
  const m = String(date.getMonth() + 1).padStart(2, "0");
  const d = String(date.getDate()).padStart(2, "0");
  return `${y}-${m}-${d}`;
};

// src/components/UserList.tsx
import { formatDate } from "@/utils/dateFormat";

// src/components/OrderHistory.tsx
import { formatDate } from "@/utils/dateFormat";

Example Issue:

## Overview
Date formatting logic is duplicated across multiple components.
Extract it into a shared utility function.

## Target Files
- src/components/UserList.tsx (formatDate function)
- src/components/OrderHistory.tsx (formatDate function)

## Expected Results
- Create shared function in src/utils/dateFormat.ts
- Update existing call sites to use imports
- Add unit tests for the shared function

Tip: Being specific about where duplication exists helps AI accurately scope the consolidation.

2. Design Improvement (Separation of Concerns)

A "God class" that handles too many responsibilities is a classic example of code that's hard to test and fragile to change.

Example Issue:

## Overview
The OrderService class handles order processing, inventory management,
email notifications, and logging -- exceeding 800 lines.
Separate responsibilities into distinct classes.

## Current Problems
- Violates the Single Responsibility Principle
- Difficult to test (requires extensive mocking)
- Impact of changes is hard to predict

## Expected Results
- OrderService: Core order logic only
- InventoryService: Inventory management logic
- NotificationService: Email notification logic
- Clear interfaces defined between services
- Migrate existing tests and add new ones

Tip: Including a proposed structure in the Issue guides AI's design decisions. However, avoid being overly prescriptive -- provide direction, not a complete specification, so AI can leverage its design capabilities.

3. Naming and Convention Unification

A codebase with inconsistent naming conventions creates unnecessary cognitive load for every developer who reads it.

Example Issue:

## Overview
Files under src/api/ have inconsistent naming conventions.
Rename to match the project's coding standards.

## Current Problems
- Function names: mix of camelCase and snake_case
- File names: mix of PascalCase and kebab-case
- Constants: UPPER_SNAKE_CASE not consistently applied

## Project Conventions
- Functions/variables: camelCase
- File names: kebab-case
- Constants: UPPER_SNAKE_CASE
- Types/classes: PascalCase

## Target Directory
src/api/

## Constraints
- Do not change externally exposed API endpoint names
- Update all import paths accordingly

4. Performance Improvements

For performance-related refactoring, clearly defining measurement criteria is essential.

Example Issue:

## Overview
Improve the initial load time of the dashboard screen.

## Current Problems
- Three API calls executed sequentially in useEffect
- Unnecessary re-renders on component mount
- No virtual scrolling for large lists

## Expected Results
- Parallelize API calls using Promise.all
- Suppress re-renders with useMemo / useCallback
- Implement virtual scrolling for lists exceeding 100 items

Tip: For performance improvements, describe the specific symptoms and the direction for improvement. "Make it faster" doesn't give AI enough to work with.

Issue Sizing Guide for Refactoring

The most critical factor in successful refactoring is Issue granularity. Too large, and the impact becomes unpredictable and reviews become painful. Too small, and the number of Issues becomes unmanageable.

Sizing Guidelines

Size	Approx. Lines Changed	Examples
Small	~50 lines	Variable name unification, removing unused imports
Medium	50-200 lines	Function extraction/consolidation, applying early returns
Large	200-500 lines	Class splitting, module restructuring
Should Split	500+ lines	Recommended to split into multiple Issues

How to Split Large Refactoring

Large refactoring efforts are most effective when broken into stages.

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Benefits of staged splitting:

• Each PR is easier to review
• Easier to isolate when problems occur
• Flexibility to change direction mid-way
• Can proceed in parallel with other feature development

The Create Issue + Dev Run Pipeline

The real power of DevLoop Runner lies in combining Create Issue with Dev Run into a continuous pipeline.

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By running this cycle continuously, you can address technical debt before it accumulates into a serious problem. For example, running Create Issue between sprints or after releases and adding detected Issues to the next sprint backlog is an effective operational pattern.

Operational Tips

1. Run Create Issue regularly: Scan for technical debt weekly or bi-weekly
2. Reserve refactoring capacity: Allocate dedicated time within each sprint
3. Start small: Begin with low-risk items like naming improvements or deduplication
4. Visualize progress: Share technical debt reduction with the team to maintain motivation

Tests: The Safety Net for Refactoring

In refactoring, tests are your most important safety net.

How Dev Run Protects Tests

Dev Run's 10-phase structure is particularly effective for preserving test integrity during refactoring:

1. Test Scenario Phase: Sumire reviews existing test coverage and designs additional tests
2. Test Implementation Phase: Test code is written based on the designed scenarios
3. Testing Phase: All tests are run against the refactored code

This flow automatically verifies that "existing behavior hasn't changed after refactoring."

When Tests Don't Exist Yet

When refactoring code without existing tests, a two-step approach is recommended.

Step 1: Add Tests First

## Overview
Before refactoring OrderService,
add tests that capture its current behavior.

## Target
- src/services/OrderService.ts

## Requirements
- Create unit tests for all public methods
- Cover both happy paths and key error cases
- Tests should document current behavior as-is

Step 2: Then Refactor

With tests in place, you can refactor with confidence.

This two-step approach may seem like extra work, but it dramatically reduces regression risk during refactoring, making it more efficient overall.

Writing Effective Refactoring Issues

What Good Issues Include

1. Specific problem description: Which file, which part, and why it's a problem
2. Scoped target: Explicitly state which files or modules will change
3. Defined expected outcome: Describe the ideal state after refactoring
4. Stated constraints: API compatibility, performance requirements, etc.
5. References: Mention design patterns or conventions you want applied

Vague Issues to Avoid

Bad Example	Why It's Bad	Better Version
"Make the code better"	Unclear what to improve	"Consolidate duplicate logic in OrderService"
"Refactor everything"	Scope too broad, quality drops	"Unify naming conventions in src/api/"
"Improve performance"	No measurement criteria	"Speed up list page initial render (parallelize API calls)"
"Clean it up"	No clear standard	"Reduce nesting to 3 levels max, apply early returns"

Post-Refactoring Verification Checklist

After Dev Run generates a PR, run through this checklist.

Functional Checks

• All existing tests pass
• New tests have been added
• TypeScript type checks pass without errors
• No lint errors

Design Checks

• Changes stay within the Issue scope
• No unnecessary changes included
• Naming is appropriate and consistent
• Dependencies are clear with no circular references

Operational Checks

• Public API compatibility is preserved
• Configuration changes are properly documented
• No negative performance impact

If everything looks good, use Finalize to convert the draft PR to a public PR and merge it.

Summary

• Technical debt gets more expensive the longer you wait: Use Create Issue regularly to detect refactoring candidates
• Issue sizing is key: Keep changes around 200 lines; split larger refactoring into multiple Issues
• Use category-specific approaches: Code duplication, design improvement, naming unification, and performance optimization each have their own best practices for Issue writing
• Tests are your safety net: For untested code, add tests before refactoring
• The Create Issue + Dev Run cycle: Run it continuously to maintain codebase health

Refactoring is the quintessential "important but never urgent" task. By letting DevLoop Runner's AI handle the execution while you focus on review and decision-making, you establish a sustainable approach to code quality improvement that actually gets done.