Latest Generative AI Tools and Their Usage in Mobile App Development

Generative AI is a subfield of artificial intelligence (AI) that focuses on creating new content through machine learning algorithms. Generative AI tools can be used to generate text, images, audio, video, and even code. In recent years, generative AI tools have become increasingly sophisticated and powerful. This has led to a growing interest in using generative AI in mobile app development.

AI tools for Mobile app development

Here are some of the latest generative AI tools and their potential usage in mobile app development:

  • GitHub Copilot: GitHub Copilot is an AI-powered code assistant from GitHub. This helps developers to write code faster and more efficiently. GitHub Copilot helps to generate code suggestions, complete functions, and even write entire tests. GitHub Copilot is currently only available for Android and Java development, but support for other languages is planned.
  • Amazon Code Whisperer: Amazon Code Whisperer is another AI-powered code generator assistant tool from Amazon. This¬† helps developers to write code more faster and more efficiently. It can generate code suggestions, complete functions, and even translate code from one language to another. Amazon Code Whisperer is currently only available for Java and Python development, but support for other languages is planned.
  • DALL-E 2: DALL-E 2 is a text-to-image diffusion model that can generate realistic images from text descriptions. DALL-E 2 can be used to create assets for mobile apps, such as icons, splash screens, and game graphics.
  • DiffusionBee: DiffusionBee is a text-to-image diffusion model that is similar to DALL-E 2, but it is designed to be more efficient and easier to use. DiffusionBee can be used to create assets for mobile apps, such as icons, splash screens, and game graphics.
  • Synthesia: Synthesia is a text-to-video platform that can generate realistic videos of people speaking. Synthesia can be used to create videos for mobile apps, such as tutorials, explainer videos, and product demos.

These are just a few examples of the many generative AI tools that can be used in mobile app development. As generative AI technology continues to develop, we can expect to see even more innovative and creative uses for this technology in the future.

How above tools can be used

Here are some specific examples of how these generative AI tools can be used in mobile app development:

  • Generating code for mobile apps: Generative AI tools can be used to generate code for mobile apps, which can save developers a lot of time and effort. For example, a developer could use a generative AI tool to generate a basic skeleton for a new mobile app, and then they could customize the code to meet their specific needs. This can be achieve using Github Copilot or by Amazon Code Whisperer.
  • Creating assets for mobile apps: We can use Generative AI tools to create assets for mobile apps, such as icons, splash screens, and some game graphics using DiffusionBee. This can be especially helpful for developers who do not have experience in graphic design.
  • Generating content for mobile apps: Generative AI tools can be used to generate content for mobile apps, such as blog posts, news articles, and product descriptions. This can be helpful for developers who want to create a dynamic and engaging user experience for their apps using DiffusionBee and Synthesia.

Code Security aspects about these tools

One of the biggest concerns is that generative AI tools that, these tools can be get trained on code that may contains vulnerabilities. Due to these aspect tools may suggest code that is insecure or that contains known vulnerabilities. Also , generative AI tools can be used to generate code that is complex and difficult to understand, which can make it difficult to identify and fix security vulnerabilities.

Here are some of the code security aspects of generative AI tools like GitHub Copilot and Amazon Code Whisperer:

  • Vulnerabilities in the training data: These tools are trained on massive datasets of code, which may contain vulnerabilities. So there is a risk that these tools may generate code that is also vulnerable.
  • Difficult to understand code: These tools can generate complex and sophisticated code, which may leads difficult to understand and review for security vulnerabilities.
  • Bias in the training data: Generative AI tools may be biased towards certain coding practices or frameworks, which could lead to the generation of insecure code.
  • Lack of security awareness: Some developers may not be aware of the potential security risks associated with using generative AI generated code. This could lead to them accepting code suggestions without properly reviewing them for security vulnerabilities.

To mitigate code security risks and potential issues developer should:

  • Use the tools responsibly: Developers should use generative AI tools as a productivity aid, but they should not entirely rely on them to generate secure code. Developers should always carefully review and test any code generated by these tools before using it in production.
  • Use a variety of security tools and best practices: Developers should use a variety of security tools and best practices to identify and fix security vulnerabilities in their code. This includes using static code analysis tools, code reviews, and security testing.
  • Stay up-to-date on security advisories: Developers should stay up-to-date on security advisories for the programming languages, coding practices, frameworks, and libraries they are using. This will help them to identify and fix known security vulnerabilities in their code.

Some additional thoughts on the professional use of generative AI in mobile app development:

  • Generative AI tools can be used to create personalized experiences for mobile app users. For example, a generative AI tool could be used to generate custom recommendations for users based on their interests or past behavior.
  • Generative AI tools can be used to improve the quality and accuracy of mobile apps. For example, a generative AI tool could be used to test mobile apps for any probable bugs or to generate realistic test data.
  • Generative AI tools can be used to develop new and innovative mobile apps. For example, a generative AI tool could be used to create a mobile app that can generate new art or music on demand.

So in conclusion, as generative AI technology continues to develop, we can expect to see even more innovative and transformative uses of this technology in mobile app development. The current one can be used for how code can be written is useful at initial stage, but eventually it will get more matured version of these tools.  But we need to be aware of potential code security risks associated with it. And by taking precautions mention above, developers can mitigate these risks.

Author Details

Ashay Vilas Kamble

Ashay is an experienced Senior Technology Architect with a demonstrated history of working in the information technology and services industry. Skilled in Android application development, Core Java, Kotlin, C#, Android Hardware interaction, Android OS. Xamarin platform. He has worked with cross center teams across US, Nederland, Belgium, China, Australia and London.

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