How Does AI Help Developers?
Essentially, a virtual expert fills the role of a tech lead, providing guidance, doing some of the more common tasks, conducting the code review, and validating the quality of the software. How AI assists in the development process throughout the software development lifecycle (SDLC) can be divided into three main buckets.
AI-assisted development automates common operations that are repetitive and time-consuming, including testing. Machine learning (ML) models based on millions of anonymized patterns can suggest and even choose next best steps in development. If an application fails a test, AI can also provide feedback to the developer about the areas that need to be remedied. As a result, developers are more productive and engaged, and novices benefit from the lessons learned from similar programming.
AI-assisted development includes discovery tools that analyze application dependencies, identifying violations and “red flagging” the elements (actions, screens, entities) that are assembled in the wrong place. They enforce best practices and identify duplicate code while enabling teams to keep pace with accelerated production schedules.
During the application development process, developers sometimes get stuck. AI-assisted development helps them find what they are looking for and get them back on track sooner. For instance, contextualized search experience accessed from the development environment can bring up online resources that might help to complete the task they are undertaking. And next-step suggestions walk developers through logic flows.
AI-Assisted Development Examples
Examples of AI-assisted development include:
- Automated code analysis: A tool that uses artificial intelligence to analyze source code to identify vulnerabilities, bugs, or errors. This reduces the amount of time needed for manual code review and allows developers to solve problems faster.
- Automated testing frameworks: These use AI to automate tests for web applications, mobile apps, and other software products. Developers can run tests more frequently and identify bugs before they become major problems.
- Predictive analytics: Uses AI to predict future events or trends to help businesses better understand customer and market needs so they can build the right software.
- Natural processing language (NPL): This branch of AI enables computers to understand and interpret natural language. Developers can more easily communicate with their applications, and they can build chatbots that understand what people are saying in customer service conversations.
- Computer vision: This type of AI enables machines to recognize objects in an image or video. This technology can be used for a variety of tasks in development such as facial recognition, object identification, and even autonomous vehicles.
- Automated machine learning: When machine learning is automated, models are trained quickly and accurately. Automation reduces the amount of time needed for model development and helps businesses create more accurate models in less time.
The Future of AI-Assisted Development
Elite DevOps performers are pushing out clean code into production environments multiple times a day. There is no way that this velocity can be maintained – let alone improved upon – without the automation provided by AI.
At OutSystems, our mission is to enable every company to innovate through the power of software. By adding AI to all stages of the development process, the OutSystems low-code platform helps development teams maximize their existing capabilities, freeing them to focus on the software that truly makes the difference, instead of wasting their time on just keeping the lights on. Our vision for the future of AI-assisted rapid software development includes:
- App generation using conversational prompts: Instead of writing code, developers will describe an application in natural language and generative AI will do the heavy lifting.
- Real-time visual representations of app changes: To eliminate the “black box problem” created by generative AI, the OutSystems visual language will make it easy to validate the output of generative AI.
- Extensive ecosystem of AI connectors: Customers can easily build AI-powered apps in a matter of minutes with connectors to common services from Microsoft, Google, Amazon, and other third-party providers.