10 Ways AI Can Speed Up your Mobile App Development | Guide for Developers

Introduction

Today, AI (Artificial Intelligence) technologies can speed up the mobile app development process. They can enhance mobile apps performance, and improve user experience. Developing AI powered apps is becoming more and more popular. From code generation and automated testing to user interface design and performance optimization, newer and newer app features depend on artificial intelligence and machine learning.

No matter if you’re an Android, iOS, Flutter, or Kotlin Multiplatform developer, this guide provides tips and examples to help you use the power of AI algorithms in your daily work.

My name is Karol, and I’ve been a mobile developer since 2011 (more about me in the bio below). I use AI tools every day in my work at Droids On Roids, primarily through GitHub Copilot, including chat. Moreover, I use Hemingway, ChatGPT, Claude Sonnet and Stable Diffusion.

In November 2023, I completed the AI Devs 2 course. It covered generative AI, programming AI assistants, and other exciting topics. In November 2024 I’ll attend the AI Devs 3 course. I’m excited to learn more about AI technology and how it can help me in my daily work. I want to share some popular AI tools that you’ll find valuable.

Basic concepts

There are several concepts you should understand before diving into the details of how to use AI in app development.

Large Language Models

Large Language Models (LLMs) can generate human-like text based on the input they receive. A Large Language Model (LLM) is an advanced type of artificial intelligence and machine learning. It can understand and generate human-like text based on the input it receives. These models are trained on extensive datasets, which contain a lot of text sources.

Such models can perform a variety of language-related tasks, like text completion, translation, summarization, and question-answering. LLMs leverage deep learning techniques, particularly transformer architectures, to capture the nuances and complexities of human language. This makes them powerful AI tools. GitHub Copilot and similar solutions use LLMs.

LLMs can also be embedded inside AI powered mobile apps. Current smartphones are powerful enough to handle a local LLM and the mobile apps using them do not even need to connect to the internet. If you want to read more about developing AI powered mobile apps, read my previous article: How to develop an AI app with a local model in Kotlin Multiplatform. It describes how to create a simple AI powered mobile app.

Generative AI

This type of AI technology can create new content, such as images, text, or music. It looks for patterns by analyzing user data. Generative AI can be used to create data for training machine learning models.

Generative Artificial Intelligence also enables the creation of virtual environments and simulations. It is useful for mobile apps, such as gaming and virtual reality. For example, Stable Diffusion can generate realistic images matching the text prompt.

Read also: The Key Differences Between AI vs Gen AI

Natural Language Processing

Natural Language Processing (NLP) is a field of artificial intelligence and machine learning. It focuses on the interaction between computers and humans through natural language. This involves algorithms that enable machines to understand human language. Natural Language Processing encompasses a variety of tasks, such as language translation, sentiment analysis, speech recognition, and text summarization, for example.

When it comes to AI powered mobile apps, Natural Language Processing can be used for the likes of voice-activated commands. In this case, they allow app users to perform tasks hands-free, but Natural Language Processing can also power chatbots and virtual assistants, helping AI powered mobile apps can provide instant customer support and personalized user experience.

Deep learning

Deep Learning (DL) is a subset of machine learning. It uses neural networks with many layers to model complex patterns by processing data. This creates many useful features and for mobile apps, such as image and speech recognition, natural language processing, and autonomous driving.

DL has revolutionized the mobile app development industry. It leverages machine learning algorithms to create AI apps that can perform complex tasks, including the aforementioned image recognition, as well as natural language processing, and predictive analytics. These models can enhance user experiences by providing personalized content and recommendations. For instance, AI apps can use DL to analyze user behavior and adapt the app’s functionality. Additionally, Deep Learning facilitates the development of advanced features, such as real-time language translation and augmented reality, making mobile applications more interactive and user-friendly.

How are developers using AI? Key stats

Let me share some interesting results regarding developer usage. According to the 2024 Stack Overflow Developer Survey, around 82% of developers are currently using AI tools for writing code. The next most popular uses include searching for answers, debugging, and documenting code. Interestingly, 46% of respondents want to start using AI for testing code. What’s more, nearly 40% are interested in leveraging AI for tasks like committing and reviewing code, predictive analytics, and even deployment and monitoring! Take a look at the popular uses of AI in the development according to Statista:

This highlights how AI’s role in software development is growing fast. For software houses, it’s key to stay on top of these trends and use AI in smart, responsible ways to deliver solutions that are both efficient and high-quality.

How can you use AI in mobile app development?

In this section, we will explore how you can integrate AI into mobile app development. I will cover various AI tools and virtual assistants that can enhance your app development process. Some ideas from my list overlap with the Stack Overflow survey results, but you’ll also find some unique insights. These come mainly from my own daily experiences working with AI tools. I hope you’ll find them helpful!

AI-powered code generation

When it comes to improving mobile app development, AI-powered code generation helps automate repetitive tasks and provide suggestions. Tools like GitHub Copilot offer automated code completion. They help app developers write code faster and with fewer errors.

Additionally, AI can generate boilerplate code, which is common in app development. In the case of mobile apps, it can be used when setting up a new project. It is also good at generating repetitive code fragments like subsequent form fields. Such tools can save developers a significant amount of time and effort. Codeium and Tabnine, for instance, provide smart code completions. They base their predictions on the context of the written code. Most of these tools are available as plugins for popular IDEs used by mobile app developers, like Android Studio or Xcode.

To get the most out of GitHub Copilot, use clear and descriptive comments. Look at the official article about tips, tricks, and best practices. It’ll guide the AI in generating relevant code snippets. Don’t forget to review the generated code and ensure it meets your project’s requirements and standards.

AI-assisted testing and debugging

AI-assisted testing and debugging tools automate various testing tasks and related work. There are test case generation solutions, such as Testim and Applitools, that use AI to create test cases, ensuring better coverage and reducing manual effort.

AI tools mentioned in the previous section can help in writing unit tests. They can also generate other types of tests written as code, such as integration and end-to-end tests.

Tools like DeepCode analyze code to identify potential bugs before they occur. They help developers fix issues early in the app development cycle, which can significantly reduce operational costs and the overall time spent on debugging.

AI-driven user interface design

Tools like Uizard and Sketch2Code generate UI elements based on design specifications. They can convert design mockups into functional code, reducing manual effort. AI can also optimize layouts for different screen sizes. Tools like Framer, for instance, use AI this way. It adjusts UI components to fit different screen dimensions.

Additionally, AI can suggest design improvements based on user experience data. It helps developers create more intuitive and user-friendly interfaces.

Tools like Stable Diffusion and MidJourney can generate realistic images. They do that basing on textual descriptions. Those tools can reduce the time spent on manual design tasks. This enables development team members to focus on the more creative aspects of mobile app development.

Uizard uses AI in its design-to-code feature. It can convert your design mockups into functional UI code. Additionally, its suggestions can optimize your layouts for different screen sizes and orientations.

To get the most out of Stable Diffusion, focus on crafting detailed, specific text prompts. They should precisely describe the desired output.

Natural Language Processing for app localization

Translation tools like Lokalise and Transifex have advanced Natural Language Processing capabilities nowadays. They can translate a mobile app’s content into many languages. Such translations are not only accurate but also culturally relevant. They can be used not only inside mobile applications but also for text in the app stores.

Tools like Phrase and Smartling also use Natural Language Processing. They consider the context in which the text appears. When using Smartling, integrate it early in the app development process. That way, it can capture content as it’s created

AI-powered performance optimization

AI technology helps in resource allocation and management. Tools such as Kubernetes can dynamically allocate resources based on current demand. This ensures optimal performance and cost efficiency.

Tools like Akamas, similarly, can determine the caching mechanisms to reduce load times. Then, they balance the loads across servers, helping to maintain a high responsiveness and prevent bottlenecks.

AI-enhanced security implementation

There are automated vulnerability detection and patching tools, such as Snyk and Mend.io. They can constantly scan codebases for app security vulnerabilities and apply patches automatically. Consequently, they reduce the risk of exploitation.

Auth0 by Okta provides Intelligent user authentication systems. It uses AI technology to analyze user behavior and implement adaptive authentication mechanisms. It can help ensure secure access while minimizing friction for legitimate users.

Threat analysis and prevention tools, such as Darktrace and Vectra, also use AI algorithms. They monitor network traffic and detect anomalies. They can help with proactive threat mitigation.

AI-driven user behavior analysis and personalization

Predictive user modeling tools, such as Mixpanel and Amplitude, analyze user behavior data. They can forecast future users’ actions and preferences. Automated A/B testing tools, like Optimizely and Google Optimize, use AI. They can run experiments, predict user actions and identify the most effective variations.

Content recommendation systems, like those provided by Recombee and Algolia, also utilize AI algorithms. They analyze users’ behavior, interactions and preferences to suggest relevant content. For example, let’s assume that a user watches several action movies. The AI will recommend other action movies or related genres in real-time. Or, let’s say that a user listens to music on their mobile device during the morning commute. The AI will suggest similar music during that time frame.

AI-assisted API integration and management

Tools such as New Relic and Dynatrace also use AI algorithms. They do this for API performance monitoring and optimization. They check API performance, detect anomalies, and provide actionable insights for optimization.

AI-powered asset management and optimization

Media compression tools, such as Cloudinary, use AI as well. They reduce file sizes without compromising quality. Akamai and Fastly utilize AI algorithms to manage versions of assets and cache them effectively. This can reduce server load and improve delivery speed.

AWS CloudFront use AI to analyze user behavior. It can then load the most relevant resources faster, resulting in optimized performance and user engagement.

AI-enhanced collaboration and project management

Asana and Trello use AI to assign tasks based on team members’ skills and availability. Monday and Jira, likewise, use AI to track project milestones. They generate real-time status reports, and provide insights into project health.

Deadline and resource management tools, such as ClickUp and Wrike, are other noteworthy examples. They employ AI to forecast project timelines, help identify potential bottlenecks, and suggest resource adjustments to meet deadlines.

Let’s sum up what we’ve discussed in this part:

Wrap-up

AI is revolutionizing mobile app development by enhancing various aspects of the process. It has become more and more popular in the mobile app industry. For example, AI-powered code generation tools like GitHub Copilot speed up coding, while graphic tools like Stable Diffusion generate the assets. AI can do more and more time consuming and repetitive tasks. This all leads to faster app development cycles and lesser costs. At Droids On Roids, we use AI tools such as GitHub Copilot and ChatGPT to accelerate software creation and reduce costs for our clients. 

Tips & tricks

When using AI tools, it’s important to understand the problem you’re trying to solve. You should specify the desired outcomes. The more precise you are, the better results you’ll get. Carefully check the capabilities and limitations of each AI tool. Ensure it aligns with your requirements.

Don’t forget to check the terms and conditions. Some tools may use data you enter to train the models. If that data is confidential, check if you can disable such learning. Otherwise, your data may appear to other users of the given tool.

Sometimes you may need to buy a more expensive subscription to increase privacy. Always maintain human oversight and control to ensure responsible and ethical implementation. Additionally, be prepared to experiment, and iterate.

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