Using generative artificial intelligence (AI) solutions to produce computer code helps streamline the software development process and makes it easier for developers of all skill levels to write code. The user enters a text prompt describing what the code should do, and the generative AI code development tool automatically creates the code. It can also modernize legacy code and translate code from one programming language to another.

By infusing artificial intelligence into the developer toolkit, these solutions can produce high-quality code recommendations based on the user’s input. Auto-generated code suggestions can increase developers’ productivity and optimize their workflow by providing straightforward answers, handling routine coding tasks, reducing the need to context switch and conserving mental energy. It can also help identify coding errors and potential security vulnerabilities.

How does generative AI code generation work?

Generative AI for coding is possible because of recent breakthroughs in large language model (LLM) technologies and natural language processing (NLP). It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code. Training code generally comes from publicly available code produced by open-source projects.

Programmers enter plain text prompts describing what they want the code to do. Generative AI tools suggest code snippets or full functions, streamlining the coding process by handling repetitive tasks and reducing manual coding. Generative AI can also translate code from one language to another, streamlining code conversion or modernization projects, such as updating legacy applications by transforming COBOL to Java.

Even as code produced by generative AI and LLM technologies becomes more accurate, it can still contain flaws and should be reviewed, edited and refined by people. Some generative AI for code tools automatically create unit tests to help with this.

What are the benefits of using generative AI for code?

Using AI code generation software is generally straightforward and available for many programming languages and frameworks, and it’s accessible to both developers and non-developers.

There are three main benefits of using AI code-generation software tools:

1. It saves time by enabling developers to generate code faster, reducing the work of manually writing lines of code and freeing developers to focus on higher-value work.
2. Generative AI can quickly and efficiently test and debug computer code.
3. Using generative AI for code also makes code development accessible to non-developers.

How does generative AI for code differ from low- and no-code?

Generative AI, low-code and no-code all provide ways to generate code quickly. However, low-code and no-code tools depend on prebuilt templates and libraries of components. The tools enable people without coding skills to use visual interfaces and intuitive controls like drag-and-drop to create and modify applications quickly and efficiently while the actual code remains hidden in the background.

Generative AI for code software, on the other hand, doesn’t use templates and libraries of components. The software reads a developer’s plain language prompts and suggests code snippets from scratch that will produce the desired results.

While low-code and no-code tools generally target non-developers and business users, both professional developers and other users can use AI code-generation software.

Examples of currently available generative AI code generation tools

• IBM watsonx Code Assistant: IBM watsonx Code Assistant helps developers write code using AI-generated recommendations, no matter their experience level. Developers can make requests in plain language or use existing source code to generate code for targeted use cases. Out-of-the-box, watsonx Code Assistant provides pre-trained models based on specific programming languages to ensure trust and efficiency for accurate code generation.
• Github Copilot: Github Copilot is a pre-trained AI model and code completion tool that writes code in many languages, including JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and works with HTML and CSS. It uses machine learning to suggest code based on context, can analyze your code for vulnerabilities and is available as an extension for integrated development environments (IDEs) like Visual Studio Code, Visual Studio, Neovim and JetBrains. GitHub Copilot uses publicly available code from GitHub repositories and is powered by OpenAI Codex, based on GPT-3.
• TabNine: TabNine is an AI code assistant that learns from the codebase being worked on and provides real-time code completion, chat and code generation. It includes code formatting, language detection and documentation. TabNine supports Java, Python, JavaScript, SQL and other popular languages, and it integrates into code editors like VSCode, IntelliJ and PyCharm.
• Other generative AI coding tools: Other generative AI applications include Ask Codi, CodeT5, WPCode, Codeium, CodePal and mutable.ai.

General-purpose generative AI applications

General-purpose generative AI applications such as ChatGPT from OpenAI and Google BARD also generate code based on text prompts. ChatGPT, Bard and other conversational AI applications are freestanding tools rather than integrated plugins that work directly in a developer’s own environments.

Enterprise-grade AI code generation and IBM

As mentioned above, IBM watsonx Code Assistant uses generative AI to help increase developer productivity with AI-recommended code based on natural language inputs or existing source code. With watsonx Code Assistant, users can lessen the burden of cognitive switching and reduce coding complexity, enabling development teams to focus on mission-critical work.

Purpose-built for targeted use cases, watsonx Code Assistant provides pre-trained, curated models based on specific programming languages to ensure trust and efficiency for accurate code generation. This solution allows you to customize the underlying foundation models with your own training data, standards and best practices to achieve tailored results while providing visibility into the origin of generated code.