Dec 22, 2023
min read

Applying AI in Software Development: Here's Everything You Need to Know

Boost your software development process with cutting-edge AI tools. Read this article to learn how.

Applying AI in Software Development: Here's Everything You Need to Know

At the breaking dawn of the artificial intelligence age, businesses are chasing the implementation of AI-based features in their routines. Those who will be among the first will dominate the market. Therefore, we would like to ease this path for you as much as possible.

Being ahead of the crowd, Uni, a digital creative agency, has already applied AI in development. In this article, we disclose our insights and possible drawbacks for you to boost your business performance utterly.

Interested in AI-powered features for your projects? Let’s dive in.

What is AI?

Artificial Intelligence (AI) is an advanced self-learning algorithm allowing computers to perform tasks that typically require human attention. Traditionally, such tasks as understanding natural language, recognizing patterns, and making decisions are crushing for machines; so AI is specially designed for such purposes.

In terms of AI in software development, the main goal is to improve performance to the greatest extent while maintaining the high quality of the code. AI simplifies, reduces, and automates the process immensely.

However, it’s not a magic wand; it’s an instrument requiring a skilled approach. We expect that new AI-powered tools enhance workflows; however, we must always keep some drawbacks in mind and constantly verify the result. In particular, the AI-produced outputs could seem accurate, while they aren't.

Read further to discover more about the best-proven techniques with AI to benefit our software development workflow.

AI-powered Strategies

AI-powered features can be handy in all stages of software development. Let’s consider them one by one, from start to finish.



As prototyping is an essential stage of software development, no one would miss the opportunity to accelerate it. AI in web development, especially, has become an irreplaceable tool.

Foremost, you need something like Wireframe Designer, a Figma plugin that helps deliver high-quality UX wireframes after your prompts. Such tools impel the creation and management of the user interfaces for the web or apps. They generate texts coherently, incorporate icons, and add appropriate carousels.

Similarly, PicFinder, like the renowned Midjourney app, generates images after the description. It offers customizable canvas ratios, while Simplified generates personalized icons with ease.

Furthermore, AI automates tedious tasks like renaming layers. For instance, Autoname, a Figma plugin by Diagram, automatically names elements based on the model's structure. It saves time and gives the whole project a more professional appearance.

As a cherry on top, LogRocket is a tool for analyzing prototypes. With it, designers visualize struggles, see adoption issues, and combine qualitative and quantitative data to create unique digital experiences without lengthy interviewing.

In short, AI for prototyping shortens development cycles, boosts creativity, and results in resilient and user-centric software solutions.

Code Explanation


As part of the software development life cycle, code explanation reveals the code's intended purpose, logic, and structure. So, machine learning (ML) and natural language processing (NLP) form the backbone of any AI-powered code explanation system.

There are plenty of AI-powered code explanation tools. In most cases, AI code tools both write the code and code explanations to it. Thus, AI in learning and development analyzes code and explains it to humans automatically, easing maintenance, reuse, and overall product enhancements. In other words, AI-driven code explanation:

  • improves the onboarding of new developers by enlightening complicated codebases to them;
  • standardizes code discussions and team communication;
  • adapts code explanations for the requirements and preferences of the specific development team.

Together, AI-powered code explanation invests in stable and predictable workflows for delivering products within time and budget.

Enhanced Automated Testing


Although automated testing is faster than manual on average, AI-powered testing outperforms it. It lacks the usual downsides of automated testing, like broader use cases, false results, elongated learning curves, etc. Therefore, AI-powered testing tools are essential for many fields, such as AI in game development.

AI-enabled testing tools significantly improve all stages of the Software Testing Life Cycle (STLC). Generally, they speed up test script creation by analyzing requirements, user stories, and systems in-depth. Then, with NLP techniques, they prepare templates for common testing scenarios and generate complete test scripts from prompts.

And that’s not all. AI-augmented testing tools generate detailed test data, perform data-related tasks, and automate test execution. They organize test cases, identify necessary tests across devices, and initiate regression tests for critical features.

In summary, any of the AI-powered testing platforms like Katalon Platform, TestCraft by Perfecto, or Applitools are more autonomous and reliable than traditional testing and quality assurance systems. Faster code analysis, vulnerability identification, and scenario simulation make them a valuable asset for any software development process.

Code Refactoring and Debugging

Improving code quality and maintainability has never been easier than before with AI-powered code refactoring systems. With pattern recognition techniques and machine learning algorithms, AI in mobile app development, for example, is a must.

These systems are more direct in code restructuring, variable renaming, duplicate code removal, and design pattern application than traditional tools. But their advantages are far broader than those:

  • automatic detection methods make it possible for developers to solve bugs, slow performance, and difficulty maintaining codebases before they ever occur, leading to cleaner and simpler code;
  • AI-powered refactoring activities based on industry best practices and coding conventions enhance code readability, modularity, and efficiency;
  • AI-powered tools study software development techniques and code repositories to spot patterns and propose refactoring solutions on their basis.

Usually, AI-based coding tools have refactoring and debugging features, so we will not name the examples. However, all AI-powered refactoring tools enhance software quality greatly by scanning huge codebases and learning from past bug data. They ultimately speed up bug detection and keep bugs from end users.

Technical Documentation


Technical documentation is a set of rules and regulations for using a certain piece of software. With automated data extraction and contextual explanations for modules, functions, and code snippets given by AI-powered documentation tools, technical writers enormously streamline and automate the process.

Moreover, automated systems analyze code repositories and keep track of code modifications, automatically updating relevant documentation pages to match the current codebase.

Similarly, machine learning techniques are a game-changer in terms of language. They adapt texts to technical and non-technical readers, clarifying technical terms, explaining definitions, and enlightening complex notions with explanations.

Together, these make AI-powered tools like Snappify, ClickUp AI, and Atlassian Confluence irreplaceable in spotless documentation in many spheres, namely AI in financial software development.



Copilots are digital assistants aiding users in their activities. Copilots are widely used in many fields beyond AI and ML in financial software development, including manufacturing, transportation, aviation, etc. Using AI-powered copilots leaves less room for human mistakes and enhances productivity.

There are a few examples of AI technologies to aid developers.

GitHub Copilot is an AI-powered coding tool. While developers work in their chosen code editor, GitHub Copilot delivers code recommendations based on GPT-3. Furthermore, automating mundane processes boosts efficiency and saves time. Another AI-powered tool facilitating code scripting is Codeium.

With similar AI technologies, developers get support as they code. Although these tools provide code smart samples and ideas, human attention is essential to guarantee code quality, follow best practices, and meet project objectives.

Best 5 AI-powered Development Platforms in 2024

Since we described the approaches of AI in development, it’s time to reveal several suitable platforms, too. Indeed, they are not all of them. Nevertheless, we showcase only the most powerful and reliable ones. So, let’s get down to it.

Microsoft Azure


For large-scale AI solutions, businesses can use Microsoft Azure AI. It’s an enterprise-ready cloud computing platform with integrated AI capabilities, including machine learning, deep learning, cognitive search, speech services, and many more. Microsoft Azure is a robust choice for large corporations and developing businesses.

Google Cloud


Google Cloud provides an adaptable and scalable infrastructure for building, training, and deploying machine-learning models, from data preparation and model training to hyperparameter tweaking and model deployment. It’s massive, reliable, and affordable, making it a powerful tool in your software development routine.

IBM Watson


Watson platform is the most formidable among IBM's many AI offerings. It integrates NLP, ML, and data analytics features to provide cutting-edge cognitive computing and data analytics services. IBM Watson APIs include speech-to-text and text-to-speech features, language translation, natural language understanding, visual recognition, and many others.



AWS AI/ML services are a part of Amazon Web Services (AWS) that offer AI and ML features. With them, businesses analyze data, get insights, and create smart apps. Moreover, you may use a pay-as-you-go model instead of buying pricey software. As a result, many useful and straightforward functions make AWS a good choice as an AI in learning and development.



Challenges and Limitations of AI

As we mentioned above, the AI-powered tools are not perfect. Let’s consider closely which negatives you might face while implementing AI in software development.

Extra Complexity

As the number of programs using AI increases, there will always be a trap to over-rely on machines. An even bigger concern is that once AI-generated content is implemented, it will be far more difficult for humans to update and fix it.

Besides, AI can’t handle its own software maintenance. So, when the AI malfunctions, it will be up to you and your team to find a fix.

Dependence on Data

For its models, AI applies massive data sets. Otherwise, it will produce an irrelevant outcome. So, you must always watch the data amount and credibility for AI learning and training, especially, if you're not using a public model, such as when dealing with your company's exclusive domain knowledge.

Moreover, even if AI-powered tools are fast and brilliant, they'll still make mistakes and leave you a mess once they depart. Prepare to keep up manually after your AI fails to cope with unpredicted challenges.

Moral Considerations 

Developers are not taught to handle moral dilemmas. When it comes to applying computer sciences to real-world problems, they may struggle.

Therefore, businesses using AI should be prepared to teach their technical staff about societal impact concerns. Another option is to hire consultants for developers in navigating the unexplored human aspects landscape.


There is a lot of buzz about the potential benefits of AI in software development. With the help of artificial intelligence sofware development has become more efficient, accurate, and adaptive.

While there are no limits to the possibilities for work automation, code optimization, and user experience enhancement; however, AI is not a panacea. One must be well-versed in the technology's strengths and weaknesses to integrate AI effectively.

At Uni, we have a knack for installing AI features in our everyday routine. If you need a hand in your project, we’re here to help.


How is AI the most effectively used in software development?

Some best practices include automating monotonous operations, improving accuracy, and empowering developers to construct smarter apps. Moreover, AI-powered tools assist in testing, debugging, and code optimization. The list is developing with time.

Which computer languages are used for AI?

Although there are many AI-powered resources and tools for every programming language, the most common are C++, Python, Java, and JavaScript.

Where can I begin using AI in my software development projects?

First, determine where AI can make the most difference in your software development process. The next step is gathering training data, investigating applicable AI libraries, and conducting relevant research. Remember that using AI can enhance your current infrastructure and acquire new skills for your staff. If you need guidance, you can always address our developer team.

Igor Marga
Tech Lead
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