During software development, AI code generation helps to optimize the process by offering autocomplete predictions for boilerplate and repetitive coding patterns. These code suggestions save time and effort since developers aren’t searching the internet for that information.

Because it uses natural language processing to generate code and AI capabilities to detect potential bugs, AI that writes code enables developers to ship software faster by helping them to analyze code, identify potential issues, and suggest tests for the code.

Aside from code generation, AI helps to increase speed and productivity throughout the development process. AI enables developers to:

• Streamline processes by automating repetitive or mundane tasks.

• Test user scenarios at scale.

• Provide real-time feedback on how customers interact with software.

• Minimize human biases and errors when using analytics for decision making.

How AI code generation works

AI code generation uses algorithms that are trained on existing source code—typically produced by open source projects for public use—and generates code based on those examples. Large language models (LLMs) are able to process and understand language, generate text, answer questions, and learn patterns and relationships in language to help predict text.

Currently, AI code generation works in three ways:

• A developer starts typing code and AI will try to autocomplete the code.

• A developer writes a comment in natural language and AI generates a suggestion based on what the developer wants to accomplish.

• A developer chats directly with AI; for example, asking it to write something specific or fix a bug.

When an AI code tool is put into action, it considers the context of comments and code to suggest more lines of code. For example, GitHub Copilot is a pretrained AI model that was created by GitHub using the OpenAI Codex model. It considers the text in a developer’s code editor to provide contextualized suggestions. As more models become available, context will come from more sources like a private codebase that results in fine-tuned suggestions.

It bears repeating: AI generated code isn’t always perfect, but it’s a starting point for developers. As with any other AI code tool, the suggested code should be tested and reviewed by developers (humans!).

Benefits of AI code generation

The primary benefit of AI code generation is the ability to write code in less time. Routine coding tasks and writing tests are handled by AI, leaving developers free to focus on work that involves critical thinking and problem solving.

In a survey of more than 2,000 developers, those who reported the highest productivity gains from using GitHub Copilot were also the ones who had accepted the largest number of code suggestions.

Additional GitHub research shows that when using GitHub Copilot, 74% of developers reported being able to focus on more satisfying work, 88% reported being more productive, and 96% reported being able to complete repetitive tasks faster. Being able to stay on task, make meaningful progress, and feel good at the end of the workday all contribute to developer productivity and satisfaction.

Another benefit of AI code generation is that it uses testing tools that catch and provide suggestions to fix bugs prior to deployment. These tools examine the code structure and recommend security enhancements.

How AI code generation is used in the development process

AI and AI code generation are making their mark throughout the software development process. Examples of how they work to generate better software faster include:

• Improving requirements documentation.

  Gathering, validating, and keeping track of software requirements gets messy, but AI makes it possible to get these tasks right the first time. AI helps to identify ambiguous or incomplete requirements and offers suggestions for improvement; therefore, developers are able to enhance the quality of requirements as they write them.

• Analyzing source code.

  Using algorithms that are trained on code from open source projects is inherent to AI code generation. Based on historical data, algorithms detect source code patterns that are likely to introduce bugs. AI is able to suggest tests for code changes that help to maximize code quality.

• Suggesting code.

  Understanding the context of code enables AI code generation tools to suggest lines of code and entire functions. If users choose, these tools can also incorporate users’ suggestions for improvements or corrections, which results in code that can be produced faster while still being readable and easy to maintain.

• Automating testing.

  Testing is used to verify that software does what it’s supposed to do. Generative AI helps developers write tests faster and with more consistency and reliability. When code is merged into source code, these tests are implemented automatically.

AI code generation tools streamline the development process by enabling developers to code faster. Using natural language to explain what the software should do and having that converted into code is a massive time saver for developers. AI technology also helps development teams create realistic timelines based on historical productivity data.

Examples of how developers are using AI code generation include:

• Correcting spelling and syntax errors

• Matching patterns with regular expressions

• Upleveling coding skills for current or future jobs

• Translating code from one programming language to another

While AI code tools are capable of translating from one programming language to another, not every tool supports every programming language.

The impact of AI code generation on software development

AI code generation supports developer productivity and has increased the speed of software deployments. Developers are able to spend less time typing and creating tests from scratch, which gives them more time to be creative and explore new ideas for features and functionality.

AI is certainly changing the way code gets written and it’s quickly gaining popularity in open source software development and within various enterprises. With tools that help generate new code and documentation, translate from one programming language to another, and reduce the drudgery and repetition in coding, developers won’t know how they ever got along without an AI coding assistant.

As more open source code and LLMs are improved upon, AI algorithms will become increasingly more accurate and more efficient. Developers will be able to quickly generate suggestions for lines of code and functions, leaving them more time to focus on higher-level innovation.

Bring AI-powered coding to your organization with GitHub Copilot

Give your development teams an AI pair programmer that suggests code as they type and turns natural language prompts into coding suggestions. GitHub Copilot draws context from comments and code to instantly suggest lines of code and entire functions, enabling developers to code faster and focus on business logic over boilerplate.