AI for Web Development Expanding the Capabilities of Human Developers

AI has altered the web development landscape since tools like ChatGPT went mainstream. However, enterprises thinking of using AI to completely replace the knowledge and expertise of software developers, particularly for tasks that require custom development and implementation, may be in for a rude awakening.

What AI Means In 2024

Artificial intelligence, or simply AI, is any technology that enables machines to simulate human intelligence. In computer science, AI often includes machine learning and deep learning capabilities and the development of algorithms. These capabilities enable AI to solve problems similar to how humans solve them.

However, like any other technology, AI is a tool to help humans perform jobs faster or more efficiently, not necessarily replace them.

The most common iteration of this tool today is generative AI, an AI model that generates content based on prompts. The OpenAI product ChatGPT is well-known, but others, such as DALL-E, can deliver images, text, and other media with a simple prompt.

Microsoft, GitHub, Amazon, and others have also released a series of copilot products to continue bringing AI to the mainstream public, especially developers.

Popular AI Tools For Developers

Some of the most popular AI tools for developers include:

• GitHub CoPilot: GitHub Copilot is an AI programmer that supports developers by providing autocomplete-style suggestions as they code. GitHub Copilot provides suggestions either as developers start to code or with a prompt that describes what the code should do. CoPilot also analyzes the context in the edited file and how it relates to other files.

• Amazon Q Developer: Amazon Q Developer is a generative AI assistant that can understand, build, extend, and operate AWS applications. It can answer questions about AWS architecture, best practices, documentation, and resources. Amazon Q also offers an IDE for software development assistance, generating new code, scanning for vulnerabilities, and upgrading.

• Tabnine: Tabnine is an AI code assistant that simplifies software development while maintaining privacy, security, and code compliance. Developers can use it to boost velocity, improve code quality, and automate workflows.

• ChatGPT: Arguably the most recognizable tool on the list, ChatGPT is a generative AI tool developed by OpenAI that can assist developers with various tasks, from assembling code to checking for bugs, composing articles, and more.

• Figma: Figma is a collaboration tool for UI/UX design that developers and designers can use to create, share, and test designs for various digital products, including websites and mobile apps. Figma offers several AI plugins to improve workflows, turn design prototypes into functional code, and more.

• Visual Studio: Visual Studio is an IDE for Windows .NET and C++ developers. It also includes a stand-alone source code editor known as Visual Studio Code, which supports a variety of programming languages and can run on Windows, macOS, and Linux.

The Oshyn Experiments

With the assortment of AI tools available, businesses may wonder which tools are viable solutions and how useful they are for speeding up software development processes. They may even ask, “Can AI replace software engineers?”.

We ran some experiments to determine just how viable many of these tools are:

Figma and Locyfy

Figma can be combined with the Locofy plugin to convert your design into production-ready front-end code for web and mobile apps. Simply go to the plugins section, search for Locofy, and click on it.

Once installed, Locofy will appear with the step-by-step optimization screen, where the plugin will guide you through the steps necessary to fix all the issues you encounter until you get the final code.

We tested a variety of Locofy’s capabilities, including:

• Design Optimiser: To fix the design structure, apply the auto-layout Figma’s functionality and ensure that all your screen's responsiveness works properly. AI can fix all the issues automatically, or the better option is to go individually and fix everything yourself. If the design is not well structured, the AI will try to fix it, which may result in undesired results. Nevertheless, you can fix it manually at any time with the plugin or in the code.

• Tagging: This is used to define all the interactivity of your app.

• Styling and layout: This ensures that your prototype is fully responsive to different screen sizes. You can define breakpoints, layout systems, positioning, direction, and gaps.

• Add actions: This allows you to add onClick actions to your elements, such as changing the page or opening a popup. You can even add a comment that will enable the developer to easily integrate custom functionality such as API calls once you have exported the code.

Visual Studio Code

Visual Studio Code offers an AI assistant that helps when writing code. This is particularly useful for boilerplate code and after working with the code editor for some time as it learns the context and suggests how to autocomplete.

However, the AI assistant also suggests algorithms or naming conventions that aren’t always useful. In addition, the code editor lacks precision. Visual Studio Code AI assistant helps build in React, Next.js, TypeScript, and other languages; however, much context learning needs to be done.

Our Conclusions

Working with various AI tools, we discovered that while these tools may save some coding time, a lot of work needs to be done beforehand to ensure the right assets are in place and that the AI tools continually learn from your requirements. In a sense, it’s like a virtual assistant that can assist with simple and repetitive tasks but will struggle with more complex ones, leading to more time consumption when tackling anything deeper.

AI is a tool that can assist software engineers and developers with various frontend and backend development tasks. However, these tools are more likely to improve efficiency and productivity than completely replace these roles anytime soon, particularly for enterprise-grade applications.

How to Use AI in Web Development

Enterprises and developers considering AI for use in web and software development tasks may find it helpful in a few scenarios:

• Boilerplate code: AI can quickly generate standard or repetitive code so developers can focus on more complex coding.

• Repetitive tasks: AI can streamline code formatting, documentation, and other routine or repetitive tasks.

• Testing and bug detection: AI offers a way to quickly identify potential bugs and ensure that code is compiling correctly.

• Code cleanup: Code refactoring and optimization can be sped up with AI to improve performance.

• Productivity increases: AI can be useful for finding solutions to problems found in documentation to improve developer productivity.

Why Not to Use AI

While AI can be helpful in a few scenarios, it isn’t the ideal solution for nearly as many use cases as it helps.

• Need senior developer supervision: AI can be a huge productivity boost for junior developers, but in many cases, the support of a senior developer is still required to ensure that the code is working properly and the applications are built correctly. While AI is useful for productivity, it’s just as prone to inaccuracies and hallucinations that can lead developers astray.

• AI is only as good as its training: Artificial intelligence can seemingly provide the answers to many problems. However, like any developer, it’s only as good as its training. The current state of many AI models is still far from reaching the same level of trustworthiness as experienced developers.

• Security risks: Many AI solutions pose security risks to enterprises due to the public training they undergo. This can expose businesses to several risks should proprietary data fall into the wrong hands.

• Not ideal for enterprise website projects: Many enterprise website projects are complex with a variety of moving parts. That complexity is problematic for most AI solutions and can lead to more harm than good.

• Not ideal for specialized enterprise tooling: Specialized enterprise tooling, such as those sold by vendors like Sitecore, Adobe, and Optimizely, requires support from experienced senior developers and expert partners who understand the ins and outs of these solutions. Many of these platforms offer built-in AI support to speed up some marketing processes and generate content, but human experts are still needed to get the most out of those solutions.

Striking the Right Balance Between AI and Human Developers

AI is a fantastic tool that can help human developers improve their productivity, execution, and problem-solving, but it requires a great deal of oversight.

At Oshyn, we offer decades of web development and software expertise. We’ve found that AI can be an assistant, but it needs experts like Oshyn to drive the way; otherwise, it is prone to mistakes. AI use in our daily activities is minimal. Still, we continually experiment with new tools and keep up with the latest advancements, particularly with leading MarTech solutions such as Sitecore, Adobe, and Optimizely.