AI-Driven Frontend Refactoring – Achieve Better Code Quality

Gracjan Prusik

07 Apr 2025
AI-Driven Frontend Refactoring – Achieve Better Code Quality

Introduction

AI-Driven Frontend Refactoring is one of those tasks that most frontend developers tend to put off ‘for later.’ And honestly, it’s hard to blame them—it often requires time, focus, and... a fair amount of patience. But what if I told you that today, a large part of that process can be handled by artificial intelligence?

AI doesn’t just help us write new code anymore—it increasingly supports us in maintaining and improving existing codebases. With tools like ChatGPT, DeepSeek, Claude or Cursor, frontend refactoring with AI is no longer a tedious chore but a fast, semi-automated process.

In this article, I’ll show you how AI-Driven Frontend Refactoring can support frontend developers in code refactoring, improving readability, updating styles, and fixing bugs—and which specific tools are worth knowing.

Why Is Refactoring So Important?

Before we dive into the details, it’s worth asking: why do we even bother with refactoring in the first place?

  • To improve code readability
  • To make maintenance and future development easier
  • To eliminate repetitive code blocks
  • To prevent bugs before they happen

Refactoring is like cleaning your apartment—no one really enjoys it, but everyone knows it has to be done from time to time.

Now imagine you have a robot that does the cleaning for you.
That’s exactly what AI brings to the table.

AI-Driven Refactoring of Frontend Functions – From Complex to Simple

Let’s take a simple example – a function that does multiple things at once:

function handleUser(user) {
  if (user.isLoggedIn && user.token) {
    const fullName = `${user.firstName} ${user.lastName}`;
    localStorage.setItem("userToken", user.token);
    alert(`Welcome, ${fullName}!`);
  } else {
    alert("Please log in.");
  }
}

Request to AI: "Refactor the code to make it more readable and in line with best practices."

AI responds:

function isAuthenticated(user) {
  return user.isLoggedIn && user.token;
}

function getFullName(user) {
  return `${user.firstName} ${user.lastName}`;
}

function greetUser(user) {
  localStorage.setItem("userToken", user.token);
  alert(`Welcome, ${getFullName(user)}!`);
}

function handleUser(user) {
  if (isAuthenticated(user)) {
    greetUser(user);
  } else {
    alert("Please log in.");
  }
}

This code is:

  • More modular – The logic is broken down into smaller, more manageable parts (filter() and map()), making it easier to modify and extend.
  • Easier to test – With pure functions and no side effects, each part of the function can be tested independently, ensuring better maintainability and reliability.
  • Ready for further development – The structure is clean, concise, and follows best practices, making it easy to add more features or handle additional requirements in the future.

AI-Driven Refactoring of Frontend Styles – From CSS to Tailwind / Styled Components

Another typical case: you want to migrate your code from traditional CSS to a newer styling approach, such as Tailwind CSS.

Original Code (CSS):

.card {
  background: white;
  padding: 20px;
  border-radius: 8px;
  box-shadow: 0px 4px 10px rgba(0,0,0,0.1);
}

Request: "Convert this style to a Tailwind class."

AI responds:

<div class="bg-white p-5 rounded-lg shadow-md"></div>

And that's it – no need to search through the documentation, no guessing which shadow fits.

AI-Driven Simplification of Frontend Component Code

You have a component that does too much, and you’d like to break it down?

function UserProfile({ user }) {
  return (
    <div>
      <h2>{user.name}</h2>
      <p>{user.email}</p>
      <p>{user.bio}</p>
      {user.isAdmin && <span>Admin</span>}
    </div>
  );
}

AI might suggest:

const UserHeader = ({ name }) => <h2>{name}</h2>;
const UserInfo = ({ email, bio }) => (
  <>
    <p>{email}</p>
    <p>{bio}</p>
  </>
);
const AdminBadge = () => <span>Admin</span>;

function UserProfile({ user }) {
  return (
    <div>
      <UserHeader name={user.name} />
      <UserInfo email={user.email} bio={user.bio} />
      {user.isAdmin && <AdminBadge />}
    </div>
  );
}

Więcej komponentów = większa kontrola, lepsza czytelność.

Removing Dead Code and Unused Dependencies in Frontend with AI

AI can analyze the file structure and indicate which functions or imports are not being used.

Request: "Point out unused variables and functions in this file."

Example of results:

// ❌ Unused import
import { calculateAge } from "../utils";

// ❌ Unused variable
const debugMode = true;

This is especially useful in large projects – it saves time and reduces clutter.

Automating Refactoring Tests with AI – From Manual to Automated

AI can also help rewrite tests, for example, from manual console.log statements to proper unit tests (e.g., in Jest).

Code before:

console.log(getUserGreeting("Anna")); // should return "Hello, Anna!"

Code after:

test("returns greeting for user", () => {
expect(getUserGreeting("Anna")).toBe("Hello, Anna!");
});

For large applications – a huge time saver.

Tools that support AI-driven refactoring

Here are some tools that are particularly worth testing:

  • ChatGPT / Claude / DeepSeek – general support, code generation, and refactoring.
  • Github Copilot – automatic refactoring suggestions in the editor.
  • Cursor – AI-first IDE that allows refactoring, code analysis, and working with code without leaving the editor.

Summary

Refactoring frontend code with the help of AI is not the future – it's the present. Thanks to AI-powered tools, we can improve code quality faster, safer, and without the frustration.

Whether you want to:

  • organize functions,
  • simplify styles,
  • detect unused imports,
  • or generate tests – AI can help with that.

In the next part of the series, we'll take a look at:

  • Code review with AI: Which tools help analyze code?
  • Optimizing frontend application performance with AI
  • Accelerated onboarding of new developers with AI

Frontend with AI is not just about faster development – it's also cleaner, better, and more satisfying code. If you want to learn how to create components.

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!