AI in Web Development: Advantages, Challenges, Use Cases

For many years now, along with blockchain, Internet of Things, as well as virtual and augmented reality, artificial intelligence has been considered one of the main trends in software development: its extensive capabilities cover workflow automation, the generation of new content, analytics, forecasting, and much more. This success is confirmed by research by Marketsandmarkets: the AI ​​market is expected to reach $407 billion by 2027. 

At the same time, AI can benefit businesses long before their digital products are released – in particular, it can significantly speed up time to market, helping project teams work more efficiently than ever before. Actually, the AI possibilities in web development will be discussed now.

Applications of Artificial Intelligence in Web Development

So, let's find out how to use AI in web development at each stage of the software lifecycle.

Code Generation, Optimization, and Completion

Generating software code using AI is a new reality. For example, along with using website developer AI no- and low-code platforms, modern programmers also have the opportunity to directly turn to artificial intelligence to simplify and speed up the coding process. No, this does not mean that the AI ​​we know today can completely replace their work. However, it is capable of helping to make a software solution faster, easier to support, and more cost-effective (the less human resource costs, the cheaper the project). Let's look at the most advanced AI software development tools used by project teams around the world.

• IBM watsonx Code Assistant. This tool was specially created for generating code in more than 115 programming languages. To do this, specialists can use both text queries and pre-created code, for example, for the purpose of its optimization. This powerful solution is based on some of the most advanced AI models to date – IBM Granite foundation models.
• Github Copilot. This is another pre-trained OpenAI Codex model based on GPT-3, which can be used to generate and analyze program code for vulnerabilities written in JavaScript, Go, Perl, PHP, Ruby, Swift TypeScript, HTML, CSS, etc. This model relies on context dependencies and can use ready-made program code for its auto-completion. Using this solution is possible by installing an extension for the Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs – for this, you just need to share your project on GitHub.
• Gemini. This is a Google project that generates, debugs, and explains program code. Today, its capabilities cover 20+ programming languages. Gemini is based on the advanced Vertex AI model, as well as a set of Codey API models. As in previous cases, this tool supports natural language processing and also allows uploading of original program code, thereby helping developers with refactoring and bug fixing. 

UI/UX Design Creation and Enhancement

Now, let's find out how AI can help designers. You might be wondering – if your project team is using AI for web design, will your solution’s interface be based on AI ideas only (i.e., it won’t be crafted)? Don’t worry, thanks to AI capabilities, they will only be able to work on projects faster and more productively. Here are some tools that are commonly used for this purpose.

• UIzard. This is an advanced wireframing and prototyping solution that uses AI to transform hand-made sketches and screenshots into digital designs. With its simple drag-and-drop interface, designers can quickly create new UX/UI projects from ready-made, customizable components and templates. 
• Miro Assist. This tool is integrated into the comprehensive design solution Miro and is able to independently analyze everything that designers place on their boards. Thanks to this, they can generate new mindmaps, presentations, and diagrams, transform ideas and insights into algorithms and user flows, group content, and much more in no time. 
• Galileo AI. This solution integrates with Figma and is used to create custom user interfaces based on text queries. Designers usually start using it at the ideation stage to build wireframes. It is noteworthy that the generated projects are fully editable, which means that the final solution is unlikely to resemble what was created by another project team for the same request.

Automated Testing and Predictive Analytics

It is impossible to overestimate the capabilities of AI in testing and predictive analytics, which allows QA specialists to identify vulnerabilities in software code long before they damage the project owners’ businesses. So, let's find out which popular software development AI tools can help with this.

• Aqua Cloud. The main focus of this solution is the creation of test cases based on text queries. This tool also helps in building testing stages, prioritizing test cases, and automatically deleting duplicate ones. Note that the solution can be integrated with any third-party software via the REST API.
• Perfectlo Scriptless. This is a well-known solution for automated testing of web and mobile applications. In particular, by using it together with Selenium, developers can take the auto testing process to a new level, simplifying the processes preceding and following the launch of autotests (in particular, we are talking about compiling test scenarios, creating reports, etc.). This product is fully compatible with Jenkins, TeamCity, Jira, GitLab, etc.
• Testim. This tool is very similar to the previous one. However, it is more focused on creating autotests based on ready-made program code rather than writing them from scratch, based only on text queries. The main objects of testing for Testim are user interfaces and APIs, but it is no less useful in functional testing. Quality assurance specialists can use it together with Selenium.

Deployment Automation and Version Control 

Deployment automation and version control, which are usually embodied in the DevOps ideology, are difficult-to-implement practices that usually require a lot of resources and upgrades before they bring benefits to businesses. That is why the use of AI can be invaluable in adopting DevOps.

• ClickUp. This is a comprehensive cloud solution for managing teams, tools, and knowledge. In practice, teams use it to track issues, sprint backlogs, evaluate progress, build roadmaps, create documentation, generate user flows, and much more. This tool seamlessly integrates with Git.
• CodeGuru. This AI web development solution is used to automatically analyze program code in Java, JavaScript, Python, C#, TypeScript, and IaC. It provides programmers with detailed hints, helping to quickly detect and fix vulnerabilities, eliminate unnecessary dependencies and duplication, and generally improve code safety and performance, reducing resource costs for development. The solution is fully integrated with GitHub, GitLab, Bitbucket, and AWS solutions.
• Datadog. If you need a “smart” solution for monitoring application performance, pay attention to this cloud-based one. With its help, you can view traces with telemetry in an interactive monitoring panel in a couple of clicks, as well as collect and analyze code performance indicators and dependencies. The tool supports integration with SAP, Active Directory, Git, etc.

Advantages of AI in Web Development

Let's summarize the insights about the above AI-powered tools for software development teams and transform them all into their specific benefits.

• Acceleration of the development process: using AI, project teams can bring product owners' ideas to life faster, without wasting a lot of time and effort on routine tasks.

• Improving the quality of created solutions: thanks to the advanced analytical and predictive capabilities that AI provides, project teams can create higher-quality web solutions without compromising the time to market or the clients’ budgets.
• Improved user experience with a smaller project budget: reducing the time spent on user testing through the use of AI solutions allows teams to create user-centric projects in fewer iterations.
• Empowering project teams: with the help of artificial intelligence, team members can learn during the development process, closing gaps in their knowledge and using the best that digital technologies can offer in the projects they are working on here and now.

Challenges of AI and Machine Learning in Web Development 

Obviously, every coin has two sides – and AI in web development is not without such a “second” side. So, what are these challenges that can be provoked by its use?

• Lack of understanding of the global context: since any AI model uses only the data provided by the developers, it can miss the general context, which makes solving complex problems for such models almost impossible.
• Limitations of decisions made by artificial intelligence: despite the fact that modern AI models are improving every day, their focus can still be limited, which, in turn, makes them unsuitable for projects in narrow-focused business niches (at least in the context of global assistance).
• Technical limitations on the use of some AI models: some AI-driven solutions are very resource-intensive, which makes it hard to use them on user devices with limited performance and memory.

Predominant Issues Caused by AI in Web Development 

Formally, we can identify three global problems that can already be provoked today by the careless use of artificial intelligence in web development.

• Abuse of the help of “smart” solutions: project teams can use AI too actively in their work and, thus, receive a non-unique solution at the end (if their example was also followed by someone else earlier).
• “Blind” use of AI: due to unconditional trust in smart technologies and the lack of manual verification of what they have generated, businesses can receive low-quality products, which will subsequently entail additional costs for their optimization and elimination of errors.
• Problems with data privacy: while the correct approach to the use of such tools allows development teams to create ethical solutions in accordance with user data security policies, those that have not undergone preliminary training can, on the contrary, endanger this data.

The Future of Web Development with AI: Will AI Replace Web Developers?

According to the media, the future of AI and web development is quite thrilling: many experts fear that these technologies will bring more harm than good in the long term, starting with an increase in unemployment and ending with a potential threat associated with the “rebellion of the machines”.

At the same time, for now, all these thoughts are just one of thousands of versions of the evolution of this technology, and theoretically, even in a hundred years, development teams will receive more benefits from AI than potential challenges and problems.

It is also worth noting that AI is unlikely to be able to solve complex, context-dependent problems in the coming years, so its use in such projects will still be limited.