What is AI-assisted development?
AI-assisted development uses machine learning (ML) models developed from millions of anonymized code patterns to recommend the correct tools and patterns for any developer situation to the developer. It’s designed to increase focus and productivity by helping users create logic flows faster and with more confidence, even for complex tasks.
What are the benefits of AI-assisted development?
There are two main benefits to AI-assisted development:
- It makes software development more accessible to those who do not have coding skills while building on those of existing developers. With a million open developer vacancies in the US alone, AI-assisted development can help to fill this talent gap.
- It can identify and promote development best practices. As a result, AI-assisted development can help prevent poor-performing software, reduce or eliminate bugs or security vulnerabilities, and generate high-performing, future-proof, and resilient software that is future-proofed, performant, and resilient.
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.
1. Automation
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 the 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.
2. Validation
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.
3. Guidance
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 and AI-powered 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.
The latest State of Application Development report shows that 31% of organizations consider AI-assisted development an integral component of their software development practices. At OutSystems, our vision for the future of AI-powered 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.
By adding AI to all stages of the development process, the OutSystems AI-powered 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.
"Gen AI was the masterpiece behind my strategy to help solve the business problem of scaling and automating our support operations. Using our own platform, we were able to deliver a generative AI chat agent and make an immediate impact."
Paulo Garcia VP of Global Support, OutSystems
Learn the fundamentals of modern development
Frequently asked questions
Artificial intelligence enhances dev productivity by automating repetitive tasks, offering intelligent recommendations and contextual guidance, and reducing debugging and testing time.
What is the difference between AI, generative AI, and machine learning?
- Artificial intelligence: designed to mimic human intelligence and handle tasks that typically require human cognitive abilities. It learns from experience, adapts to new inputs, and executes problem-solving tasks.
- GenAI: uses AI algorithms and natural language processing to create new content from a model trained on large datasets, generating content that mirrors the original data in style or structure.
- ML: a method that enables computer systems to acquire knowledge from experience, by training algorithms using historical data to make predictions or decisions without being exactly programmed.
By automating repetitive tasks, suggesting the next steps in the development process, and provisioning feedback on failed tests, AI enables developers to work more efficiently.
ML models are functions or formulas that take input data (features) and produce outputs (predictions) based on the patterns learned from training data.
Artificial intelligence can complement and enhance human intelligence in many tasks, but it's unlikely to replace human intelligence fully.