20-Year Veteran Programmer Uses Claude Code to Refactor Legacy Code: From One Week to One Hour

A veteran automotive software developer with 20 years of experience went from DeepSeek to IDE plugins to Claude Code, completing a full journey of AI programming tool exploration. After two months of intensive use, he reached a transformative conclusion: AI doesn't replace programmers — it makes experience more valuable.

From Observer to Practitioner: Three Stages of AI Programming Tool Exploration

In early 2025, the DeepSeek boom made this veteran programmer realize that AI programming was no longer something he could avoid. So he began serious experimentation, though the path wasn't smooth — it roughly went through three stages.

Stage One: Web-based chat tools. He tried DeepSeek, Tencent Yuanbao, Doubao, and others. Simple tasks were fine, but complex requirements were beyond their capabilities — code often got messier with each iteration, and quality was hard to guarantee.

Stage Two: IDE plugins. He moved to Cline, Tongyi Lingma, and other IDE-integrated tools. Code completion was indeed more convenient, but they performed poorly on analysis and comprehension tasks, unable to handle complex code reviews and architecture analysis.

Notably, IDE plugins like Cline and Tongyi Lingma are essentially enhanced code completion tools that operate in a "single-step suggestion" mode — the developer writes code, and the AI predicts the next line or code block. Claude Code represents a different paradigm: Agent mode. In Agent mode, the AI doesn't just complete code — it can read the entire codebase, understand file dependencies, formulate multi-step execution plans, and autonomously complete a series of operations under developer supervision. This difference isn't obvious for simple tasks, but for complex tasks requiring cross-file understanding, architecture analysis, and multi-round iteration, the advantages of Agent mode are significantly amplified. This also explains why the IDE plugin stage felt "weak on analysis and comprehension tasks."

Stage Three: Claude Code. The difference on complex tasks was stark — analyzing code, identifying design problems, and providing refactoring suggestions, Claude Code's output better aligned with real engineering needs.

His core takeaway: For deterministic code completion work, there's little difference between tools; but for tasks involving understanding and intent, Claude Code is the best I've used.

Case Study One: Vehicle Control Interface Testing Tool — From One Week to One Hour

The first case that blew him away was using Claude Code to write a vehicle control interface testing tool.

Automotive Software Development is one of the most demanding areas in embedded software. Unlike regular application software, automotive software directly impacts driving safety, typically requiring compliance with ISO 26262 functional safety standards and development according to AUTOSAR (Automotive Open System Architecture) specifications. Vehicle control modules involve CAN bus, LIN bus, and other automotive communication protocols, with massive numbers of interfaces and complex state machines — any logic error could trigger a safety incident. This is why "legacy code" in the automotive domain is particularly difficult to refactor — high testing costs, long validation cycles, and complex accountability chains make technical debt especially stubborn in this industry.

In automotive development, vehicle control modules typically contain hundreds of interfaces covering Set, Get, Callback, and other types. Previously, manually writing a complete testing tool took at least one week — reviewing parameters for each interface, writing test cases, and handling various edge cases.

This time, he fed the interface documentation and related dependency packages directly to Claude Code, clearly described the requirements, and had it running in one hour with full interface coverage. His reaction was "excited yet complicated" — AI is indeed powerful, with efficiency improved by dozens of times.

However, while this case was impressive, it was essentially "accelerating manual labor." What truly changed his view on AI programming was the AC module refactoring that followed.

Case Study Two: AI-Assisted Refactoring of a 2500-Line Legacy AC Module

This is the most valuable part of the entire sharing.

A "Code Mountain" Nobody Dared Touch

The team had a legacy air conditioning control module — a textbook example of "spaghetti code":

• 2500+ lines of code crammed into a single file
• 100+ state variables scattered everywhere
• 170+ direct calls to low-level interfaces, severely coupled
• Even logic errors like using true/false to store three-state values

The concept of Technical Debt was coined by software engineer Ward Cunningham in 1992 to describe the "hidden costs" accumulated by sacrificing code quality for short-term delivery speed. A 2500-line single file, 100+ global state variables, 170+ direct low-level calls — these characteristics have a vivid name in the industry: "Big Ball of Mud," one of the most common and hardest-to-fix anti-patterns. Research shows that software development teams spend an average of 23% to 42% of their time dealing with technical debt. Even trickier, technical debt often comes with "knowledge debt" — original developers leave, business logic exists only in the code itself, and nobody can fully explain the reasoning behind certain logic. This is the fundamental reason "nobody dares touch it."

These problems caused a pile of production bugs, but nobody dared touch this module — one change could break everything, and whoever modified it took the blame. More critically, this developer himself wasn't familiar with the AC business logic.

AI-Assisted Analysis: Understanding Code Structure in Minutes

He first asked Claude Code to help understand the code. Within minutes, the AI mapped out the entire module's structure and identified multiple design problems. Surprisingly, some of these were root causes that the team had spent a long time tracking down.

Of course, the AI also made judgment errors. For example, it flagged a vehicle model compatibility check as "redundant code," when it was actually necessary adaptation logic. This perfectly demonstrates the irreplaceability of human review — identifying such false positives relies on years of accumulated engineering experience, not familiarity with the AI tool itself.

Four-Step Refactoring Plan: 600+ Tasks Executed One by One

Building on code understanding, he devised a four-step refactoring plan:

1. Build a four-layer architecture, splitting the single file into 11 modules
2. Eliminate hardcoding, extracting configurations and constants
3. Fix type errors, resolving logic defects like true/false storing three states
4. Verify and fix historical bugs

Splitting a single file into a multi-module four-layer architecture is a concrete practice of the "Separation of Concerns" principle in software engineering. In automotive software, a typical layered architecture includes: application layer (business logic), service layer (functional encapsulation), abstraction layer (hardware-independent interfaces), and driver layer (low-level implementation). The core value of this architecture: each layer only depends on the next layer's interfaces without caring about specific implementations, thereby converging 170+ direct low-level calls into limited interface points. Eliminating hardcoding and extracting configuration constants is the standard approach to reducing "Magic Numbers." After these transformations, the code's testability, maintainability, and portability all improve dramatically, also laying the foundation for subsequent unit test coverage.

The entire process involved 600+ specific tasks, with Claude Code executing them one by one while he only made architecture decisions and quality checks at key checkpoints. Two weeks later, all three vehicle models compiled successfully, and all historical bugs disappeared.

This result would be nearly unimaginable under traditional development — a developer unfamiliar with the business completing a full refactoring of a module "nobody dared touch" in just two weeks.

One-line Summary

A 20-year automotive developer proves: AI programming makes experience more valuable rather than replacing programmers.