Nowadays, many companies leverage AI to optimize processes throughout the entire software development lifecycle (SLDC). Individual developers can also benefit from AI, using it to come up with more ideas faster and automate processes that allow them to spend more time on meaningful work. This article highlights AI-powered tools that API and application developers can use to boost their productivity throughout the SLDC — from design and coding to testing and maintenance.
Part of the API and application development process involves gathering data about users. If you’re planning on building an application, you’ll want to learn about potential competition. Use AI-powered tools to gather data faster and make the research process more efficient:
- Speed up research — Large language models (LLMs) like ChatGPT can help you speed up the user experience (UX) and competitor research process. For instance, ChatGPT can look at various data sources to quickly identify direct competitors, discover issues with competing applications, and learn what users like and dislike about those apps. You can also use an LLM to identify user profiles for your app and analyze their behaviors.
- Get assistance for meetings and interviews — An LLM only gets you so far regarding user research, so you’ll still need to conduct user experience (UX) interviews and meetings with SMEs. AI-powered meeting assistants such as Airgram, Fireflies.ai, or Read.ai help you organize and keep track of meetings and interviews. Some assistants can even extract data from meeting audio and track topics.
It’s important to remember that data gathering doesn’t end once you start designing. You should also collect feedback from stakeholders throughout the entire lifecycle of your API or application. And a tool like Stoplight makes collaboration with stakeholders so much easier!
Now that you’ve finished with the initial gathering of data for your application, you can start with the design. Use AI tools to streamline and speed up areas of the design process:
- Generate design elements quickly — Applications (and websites) typically include numerous design elements, such as buttons, scrollbars, and navigation menus. They also often have visual content such as illustrations and graphics. Thanks to AI, you can find many apps that generate visual content — including UI elements — based on textual input. Examples of AI tools that benefit UI/UX designers include Designs.ai, Midjourney, Stable Diffusion, and Uizard.
- Develop API specifications and definitions efficiently — When designing APIs, we recommend an API design-first approach, which involves designing all APIs around a contract written in an API description language such as OpenAPI. An AI model like ChatGPT can help you quickly create a foundation for API specifications and definitions. For example, this blog post explains how to use ChatGPT to generate a basic API specification using RAML. You can then fine-tune your AI-generated API specification by working with stakeholders through collaborative cloud tools like the Stoplight Platform.
When it comes to building APIs, you may wonder if you should design first or code first. We discuss these approaches to building APIs in this blog post. In most cases, you’ll want to design your API first and then move on to coding.
So, you’ve tackled data gathering and design with the help of AI tools, and now you need to start coding. AI tools can help you with that too, speeding up the process while ensuring you produce accurate and efficient code:
- Enhance coding efficiency and accuracy – While an LLM can generate code quickly, it’s up to you to ensure the code’s accuracy. That’s where an AI tool like GitHub Copilot comes in. This AI tool provides coding suggestions based on the natural language prompts users enter. It helps ensure the accuracy and efficiency of the code developers create. There are also great alternatives to GitHub Copilot, such as Amazon CodeWhisperer, Codeium, and Tabnine.
- Streamline and speed up integrations — Building applications today usually involves creating integrations, many of which require coding. However, some integration platform companies have started using AI to make building integrations even easier for developers:
- Blueprint — Blueprint is a tool by Merge that allows users to quickly confirm an API’s compatibility with Merge’s Unified API. The Merge team uses the output from Blueprint to automate part of the integration building process. Blueprint is currently available in Beta.
- Boomi AI — This generative AI tool with a conversational design allows developers to create integrations, APIs, and data models quickly. The tool is also currently in Beta, and you need to request a reservation to use the tool.
- Zapier AI —Zapier’s AI tool has multiple capabilities, one of them being that it quickly generates customizable integrations (known as Zaps) based on what the user tells the tool they’d like to automate. Zapier’s AI tool is currently in Beta.
If you’re building an API, you could feed your completed source code to an LLM and then prompt it to automatically generate API documentation based on that code. Better yet, LLMs are a great way to supercharge your company’s API program.
You’ve done all the work of designing and coding an API or an application, and now you need to make sure it works as expected. Consider using AI tools to optimize your testing processes:
- Automate and scale API testing — Use AI-powered tools to automatically generate and run scripts for performance tests, such as load, soak, stress, spike, and peak tests. You could start with an LLM like ChatGPT or Bard to generate testing scripts. You could also use an AI-driven tool like Postman or Testim to generate scripts quickly and scale testing as needed.
- Improve security — Use AI tools to detect and fix vulnerabilities before attackers discover and exploit them. An AI-powered security testing solution such as Snyk helps ensure the security of your APIs or applications by automating some of the testing processes and quickly identifying potential vulnerabilities.
- Test more edge cases — AI lets you automate the execution of test scenarios so that you can discover and test edge cases for APIs and applications faster than traditional methods. Use AI-powered tools such as CodiumAI or Testsigma to dramatically speed up edge case discovery and testing.
If you’re planning on designing an API, you should consider testing your API design before you start coding. With Stoplight, you can create a mock server from an API description and then test your design using API mocking before the coding phase.
Once you’ve completed your project, you’ll need to maintain it. Many of the tools highlighted above can help you maintain your API or application efficiently. For example, AI assistants can help you manage project plans and schedules to keep you and your team focused and organized around maintenance. You’ll also need to monitor the APIs your application uses and keep track of API metrics. AI-driven API monitoring solutions such as APImetrics and Datadog allow you to measure API or application performance. Use these metrics to know when and how to properly maintain your API or application.
You Have Many Options When It Comes to AI Tools
This article highlights only a fraction of the AI tools available to developers today. As more companies integrate AI into their products, your options of tools for researching, designing, coding, testing, and maintaining APIs and applications will only improve.
What AI-driven tools do you recommend for developing APIs or applications?