Using AI in Code Refactoring: Steps and Real Costs

Recently, on a client project, the need arose to refactor a 10-year-old legacy codebase with a significant amount of accumulated technical debt. In the past, such a process would involve days of manual code analysis, dependency mapping, and worries about “if I change this, what will break?” Nowadays, thanks to artificial intelligence (AI)-powered tools, the initial steps of these processes are significantly accelerated, but it’s crucial to understand the real costs and trade-offs that come with this speed.

Using AI in code refactoring primarily involves leveraging AI tools to improve the readability, maintainability, and performance of existing code. This approach offers automated code analysis, suggestions for potential improvements, and even the ability to perform specific code transformations. However, alongside the convenience brought by this automation, there are significant costs that should not be overlooked, and the critical role of human intervention remains.

How AI Has Changed My Refactoring Processes

Since the early years of my career, refactoring large and complex systems, like a production ERP, has always been a significant mental burden for me. Especially when trying to resolve if-else spaghetti or N+1 query issues within thousands of lines of code, manually inspecting every corner of the code was challenging. With the introduction of AI tools, the initial question of “where should I even look?” has been replaced by “what is AI suggesting, and what should I fix?”

Now, before I start refactoring a module, I run AI-powered static analysis tools and wait for them to list potential code smells, repetitive code blocks, or unnecessary complexities. This allows me to focus directly on the areas that need intervention and roughly halves the time I spend on the initial analysis phase. However, I must also point out that AI’s suggestions are not always perfect, and for decisions requiring deep business knowledge, I still need to step in.

AI-Powered Refactoring Steps: Where Should I Start?

Starting AI-powered refactoring, instead of diving into a massive codebase as before, requires a more structured and iterative process. I generally follow these steps, which allows me to both leverage the power of AI and minimize potential risks.

1. Code Analysis and Smell Detection

The first step is to thoroughly analyze the existing codebase with AI-powered tools. These tools can automatically detect complex methods, duplicated code blocks, unnecessary dependencies, and potential performance issues. For example, they might indicate that a method has too many responsibilities (violating the Single Responsibility Principle) or that a database query is being made inside a loop.

The tools I use in this phase typically scan the code according to specific rules (linting rules) and provide me with a report. This report reduces what would take days of manual review to minutes. However, I must remember that these tools only show surface symptoms; root cause analysis and understanding the business context still belong to me.

2. Small, Isolated Change Suggestions

One of the areas where AI truly shines is its ability to offer small and isolated refactoring suggestions. For example, it can suggest breaking a long method into smaller, meaningful parts, improving variable names, or converting if-else blocks into switch statements or polymorphism. Such changes generally have a low risk of breaking the overall behavior of the code and can be easily verified.

In my practice, I first treat these AI suggestions as a draft. AI is particularly successful at transforming boilerplate code or standard patterns. However, in areas involving critical business logic, instead of simply copying and pasting AI’s suggestion, I use it as a starting point and combine it with my own knowledge.

3. Preserving Test Coverage

One of the biggest fears when refactoring is breaking existing functionality. Therefore, preserving test coverage is vital in the AI-powered refactoring process. If the codebase has sufficient unit and integration tests, it becomes much easier to check whether the changes made or suggested by AI pass the existing tests.

In my experience, when receiving refactored code from AI, showing the existing tests to the AI or asking the AI to generate new tests increases safety. However, the quality and coverage of tests generated by AI may not always be sufficient. For this reason, I always strive to keep my test automation processes up-to-date and robust.

4. Human Review and Validation

Despite all the conveniences offered by AI, every refactoring suggestion must be carefully reviewed and validated by a human. While AI can easily catch syntactic correctness or general patterns, it cannot fully understand the nuances of business logic, domain knowledge, or future architectural directions.

At this stage, I combine AI’s suggestions with my own experience. For example, when it suggests changing a method name, I evaluate whether this name is meaningful not only technically but also in business terms. Without this human intervention, refactoring done by AI, even if it cleans up the code in the short term, can lead to new technical debt or misunderstandings in the long run.

graph TD
  A["Codebase"] --> B["Static Analysis with AI"];
  B --> C{"Code Smells Detected?"};
  C -- Yes --> D["Small Refactoring Suggestions (AI)"];
  D --> E["Human Review and Test"];
  E -- Approved --> F["Integrate into Code"];
  E -- Rejected --> G["Manual Intervention / Prompt Improvement"];
  G --> B;
  C -- No --> H["Refactoring Completed"];
  F --> H;

The Role of Prompt Engineering and Effective Usage Tips

How efficient the results I get from AI regarding refactoring largely depends on how well I prompt it. There’s a world of difference between saying “fix this code” and saying “Refactor the following Python function to be more readable and adhere to the Single Responsibility Principle by breaking it into smaller, meaningful sub-functions. ABSOLUTELY do not alter its existing behavior or return value, and minimize performance impact. Add a brief explanation for each change you make.”

Effective prompt engineering means providing AI with clear context, constraints, and the expected output format. Especially for sensitive topics like refactoring, I pay attention to the following:

• Stating a Clear Goal: I explicitly state what kind of refactoring I want (e.g., “extract method”, “rename variable”, “simplify conditional logic”).
• Sharing Existing Tests: When asking AI for refactored code, if possible, I include relevant unit tests in the prompt. This allows AI to internally check if the generated code passes the existing tests.
• Providing Constraints: For example, constraints like “minimize performance impact,” “do not add new dependencies,” or “do not change the existing interface” help AI provide more targeted suggestions.
• Defining Output Format: Sometimes I might request not only the code but also an explanation of the changes or output in a git diff format.

For example, I might try to improve a complex Python function with a prompt like this:

"""
Refactor the following Python function.
My goal: To increase the readability of the function and make it adhere to the Single Responsibility Principle.
ABSOLUTELY do not alter its existing behavior and return value.
Add short comments explaining the changes.

Function:
def process_order(order_id, items, customer_info, payment_details):
    # Order validation
    if not order_id or not items or not customer_info or not payment_details:
        raise ValueError("Missing order details.")

    # Product stock check
    for item in items:
        stock = get_product_stock(item['product_id'])
        if stock < item['quantity']:
            raise ValueError(f"Not enough stock for {item['product_id']}")
    
    # Order recording
    order_record = {
        "order_id": order_id,
        "items": items,
        "customer_info": customer_info,
        "status": "pending"
    }
    save_order_to_database(order_record)

    # Payment processing
    payment_status = process_payment(payment_details, calculate_total(items))
    if payment_status != "success":
        update_order_status(order_id, "payment_failed")
        raise PaymentError("Payment failed.")
    
    update_order_status(order_id, "completed")
    send_confirmation_email(customer_info['email'], order_record)
    
    return order_record
"""

Such detailed prompts enable AI to move beyond being just a “code generator” and act more like an “intelligent assistant.”

What Are the Real Costs of AI-Powered Refactoring?

While the speed and efficiency offered by AI-powered refactoring are dazzling, there are real costs that should not be overlooked. These costs extend beyond just financial aspects, encompassing time and risk management dimensions as well.

1. Compute and API Costs

Running large language models (LLMs) locally or using their cloud-based APIs can represent a significant cost, especially for frequent and large-scale refactoring tasks. While higher-quality models yield better results, they also increase the cost per API call. On a client project, I’ve seen AI API bills unexpectedly rise during intense refactoring periods.

This is a significant factor, especially for small teams or independent developers. To reduce costs, I’ve developed strategies such as using smaller, optimized models or only engaging AI for critical refactoring tasks. Additionally, requesting AI suggestions iteratively and in smaller chunks, rather than all at once, also helps manage costs.

2. Human Validation Time

While AI is fast at providing code suggestions, checking the accuracy of these suggestions, their alignment with business logic, and their integration with the existing system still requires human time. Sometimes, understanding and correcting AI-generated code can even take more time than writing it from scratch.

Recently, in the backend of a side project, I copied and pasted an AI refactoring suggestion and later realized it had forgotten about Redis cache invalidation in the background. Although the tests passed, the system would have behaved incorrectly in reality. Such situations highlight the dangers of blindly trusting AI and once again prove how critical the human validation step is. Regardless of the quality of the code AI provides, a review cycle should always be in place.

3. Risk of Misdirection and Technical Debt

AI can sometimes offer suggestions that are syntactically correct but semantically wrong or lead to poor architectural decisions. An AI model trained with incorrect or incomplete context might think it’s “cleaning up” the code, but in reality, it could be creating deeper technical debt. This is an insidious cost that can lead to larger problems in the future.

For example, AI might suggest converting a complex if-else chain into a shorter switch statement, but if that if-else chain has a specific order based on certain business rules, AI’s suggestion could break those business rules. In such cases, AI’s “less code is better” principle can override business context and lead to errors that are difficult to fix.

4. Lack of Context

AI models, no matter how advanced, cannot fully understand the entire business context of a software project, its organizational culture, future roadmap, or team communication dynamics. Refactoring is not just about the code itself; it also affects business processes, user experience, and even team efficiency.

AI can optimize a function, but it cannot predict whether this optimization will create a bottleneck within the overall system architecture or how it will affect another module. This lack of context limits AI’s refactoring capabilities, especially in large and distributed systems (like microservice architectures), and again highlights the critical role of human architects and developers.

Trade-offs: When to Use AI, When Is Human Intervention Better?

When incorporating AI into my refactoring processes, I need to clearly determine in which situations AI excels and in which situations human intervention is indispensable. This is a matter of balance, and making the right trade-offs is critical for project success.

Areas Where AI Shines

AI is generally very successful in repetitive, pattern-based, or syntactic refactoring tasks. For example:

• Boilerplate Code Generation and Transformation: In areas like CRUD operations, simple data transformations, or the implementation of standard patterns (e.g., Factory, Singleton), AI can generate fast and error-free code.
• Small Function/Method Improvements: For tasks like organizing variable names to improve readability, breaking long methods into smaller parts, or streamlining simple if-else blocks, AI provides good starting points.
• Code Smell Detection: It can quickly detect common code smells like duplicated code, dead code, or complex functions.
• Documentation and Commenting: AI is quite useful for adding explanatory comments or Javadoc/Docstring to existing code.

In such cases, using AI as an efficiency tool lightens my workload and allows me to focus on more strategic tasks.

Areas Where Human Intervention is Indispensable

On the other hand, there are areas where AI falls short and human expertise is absolutely necessary:

• Changes Deeply Affecting Business Logic: In a production ERP, refactoring that impacts critical business workflows like payment processing or inventory management cannot be left to AI. Such changes require in-depth analysis and decisions from someone who understands the intricacies of the business.
• Architectural Decisions and System Design: High-level architectural refactoring, such as migrating from a monolith to microservices, implementing event-sourcing, or adopting the CQRS pattern, exceeds AI’s current capabilities. These decisions have a significant impact on future scalability, maintainability, and operational costs.
• Performance Optimizations: When I see an N+1 problem in PostgreSQL, I expect AI to suggest eager loading. But sometimes I know that the planner can solve the same problem more efficiently with a different index strategy or a special JOIN mechanism; AI is not yet that good at such deep database optimizations. Similarly, system-level optimizations like Redis OOM eviction policy choices or cgroup memory.high soft limit settings require experience.
• Security-Focused Refactoring: Refactoring aimed at addressing security vulnerabilities, such as SQL injection mitigation, correct implementation of JWT/OAuth2 patterns, or rate limiting strategies, are sensitive areas where even the smallest mistake can lead to serious consequences. In such matters, AI’s suggestions should always be carefully reviewed and approved by a security expert.

Looking to the Future: The Evolution of AI and Refactoring

As AI technologies continue to evolve rapidly, their role in refactoring processes will undoubtedly change. I believe this evolution will proceed in several main directions:

First, I expect more sophisticated context awareness. Current AI models are generally limited to the code snippets they are given. Future AI tools will be able to understand the entire codebase, version control history, CI/CD pipelines, and even runtime metrics (observability data) to offer more holistic refactoring suggestions. This will allow AI to detect and solve not only syntactic but also systemic problems. For example, upon detecting an abnormal delay in a metric monitoring system, AI could directly suggest the relevant code area and potential refactoring solutions.

Second, we will see deeper integration with design patterns and architectural principles. AI will be able to offer high-level architectural suggestions like “this code can be converted to a Factory pattern” or “the responsibilities of this module can be separated to make it a better microservice candidate.” This will make AI a valuable consultant, especially in major transformations like migrating from a monolith to microservices. Perhaps one day, AI will be able to automatically suggest a CQRS or Event Sourcing architecture based on a specific business need.

Third, proactive refactoring suggestions may become standard. AI, by analyzing code usage patterns, change frequency, and error rates, will be able to detect potential code smells before they even become problems and offer refactoring suggestions. This will prevent technical debt from accumulating and support continuous improvement. After an error or performance degradation in a production environment, AI could automatically generate a refactoring suggestion for the relevant code and even open a pull request.

Finally, the importance of ethical and security-focused AI refactoring solutions will increase. We may see systems that automatically scan and correct potential security vulnerabilities or privacy breaches in AI-generated code. This will help AI not only improve code but also make it more secure and compliant. However, the potential for AI to harbor biases within itself and how these biases might be reflected in refactored code will also be a constant consideration.

Conclusion

The use of AI in code refactoring is creating a significant paradigm shift in our software development processes. AI provides us with a distinct speed advantage in initial code analysis and routine improvements. What once took days to detect code smells can now be accomplished in minutes. However, it’s crucial to remember that the conveniences offered by AI come with real costs, such as compute expenses, human validation time, and especially the lack of business logic and architectural context.

My clear position is to position AI as a “co-pilot” in the refactoring process. I fully leverage AI for tasks like transforming boilerplate code, improving small functions, or detecting common code smells. However, when it comes to critical business logic, such as payment systems, high-level architectural changes, or in-depth performance optimizations, I never cease to rely on my 20 years of field experience and human expertise. Even as AI continues to advance in the future, remembering that software architecture is not just about code, but also about organizational flows, human interaction, and strategic decisions, will remain the key to our success.