Code Rewrite & Refactoring

Every new feature addition takes months. Fixing one thing breaks another. When a developer changes, nobody understands what the code does. These symptoms indicate that technical debt has been accumulating, and over time software development becomes nearly impossible. Accumulated technical debt does not just slow down development velocity; it conceals security vulnerabilities, raises maintenance costs, and prevents you from reaching business objectives.

Our Solution Approach

At Barlas Dijital, we manage refactoring projects not with the urgency of “rewrite everything from scratch,” but through a systematic analysis and gradual transformation process. We first understand the existing code: which sections are critical, where is technical debt concentrated, and which areas are better left untouched? We build our strategy around the axes of readability, testability, and scalability. We break large changes into small, reversible steps; the system continues to run in production throughout the process.

Scope & Features

• Code quality analysis — Technical debt mapping and prioritization with ESLint, SonarQube, or Semgrep
• Architectural restructuring — Clarifying responsibilities, reducing dependencies, transitioning to a modular structure
• Test coverage increase — Unit tests (Jest, Vitest), integration tests, and regression protection setup
• Performance-focused refactoring — Resolving N+1 query problems, memory leaks, and inefficient loops
• Eliminating code duplication — Migrating repetitive logic to reusable modules following the DRY principle
• Migration to TypeScript — TypeScript integration in JavaScript projects; gaining type safety and IDE support
• Developer documentation — Clear explanations for critical modules and business rules
• CI/CD pipeline setup — Automation of the automated testing, lint control, and staging deployment flow

Technical Standards

Every refactoring project begins with measurement via static analysis and code complexity metrics; the same metrics are reported comparatively after changes. Instead of a “big bang” approach, gradual transformation using Strangler Fig or Branch by Abstraction patterns is preferred. Test coverage targets are set at 80%+ for critical modules and 60%+ for the overall codebase.

Who Is It For?

• Managers and CTOs who want to understand why every new feature development is taking so long
• New developer teams taking over a codebase and responsible for its maintenance
• Companies that want to demonstrate code quality and maintainability before an investment round or technical due diligence

Expected Outcomes

• The new feature development cycle shortens; developer productivity increases
• Bug rates and unexpected breakages drop noticeably
• New developers adapt to the project much more quickly; onboarding costs decrease
• When the interest on technical debt is paid early, development costs in subsequent periods decrease significantly