AI Tools for Code Generation

The basic capabilities of generative AI for code generation, discuss the strengths and limitations of text-generating tools for code generation, and explain the key capabilities of common models and tools for code generation. Generative AI model models and Tools for Code Generation can generate code based on natural language input. Based on deep learning and natural language processing, or NLP, these models comprehend the context and produce contextually appropriate code.

• Capabilities of Code Generators

Code generators can generate a new code snippet or a program from a text prompt. They can predict lines of code to complete a partial code snippet. They can also produce optimized versions of existing code. Further, these code generators can convert code from one programming language to another. They can also generate summaries and comments for code to improve documentation. Code generators can also recommend a complete programming solution. Describe a problem you're trying to solve, and code generators will suggest algorithms, data structures, and the right programming approaches.

Coding Through ChatGPT

Let's explore the capabilities of GPT for code-generation. OpenAI's GPT excels in human-like text generation and demonstrates impressive capabilities in code creation. Here's an example of generating simple Python code through ChatGPT, a tool based on GPT. When you enter a text prompt, "write a Python code to generate a message to greet a person." ChatGPT generates the Python code for it. Interestingly, it also provides guidelines about how to run this code. For generating code, it's important that you provide a clear prompt, specify the programming language, and provide other relevant requirements and constraints. To demonstrate how GPT helps debug code, enter an incorrect code as a text prompt in ChatGPT. An incorrect code and text are prompted to chatGPT. ChatGPT provides the correct code and explanations of the corrections made. GPT also enables translating code from one programming language to another. It can also generate code documentation and comments to improve readability. GPT based models and tools have evolved to generate longer and more accurate codes. This enables using these models and tools for coding to develop apps, websites, and plugins. Furthermore, the evolution of GPT makes it possible to generate code from images. For example, you can input the image of a course outline to generate code for a fully functional app.

Coding Through Bard

Google Bard also offers code generation and debugging capabilities in more than 20 programming languages.

• ChatGPT and Bard are valuable tools for coding for a new programming language, because they provide a step-by-step and detailed explanation for better understanding. ChatGPT and Bard excel at generating code with basic logic and programming concepts. They may not, however, be able to generate large or complex code from scratch. And while these tools understand programming concepts and syntax, they may not completely understand semantics. So, while the generated code is technically accurate, it may still not function as required. It's important to note that the knowledge of these models is limited to the data they're trained on. A specific version of GPT may not be aware of programming frameworks and libraries released after its training. For example, the knowledge of GPT 3.5 Is limited to September 2021. So if you want a more dated code generator, consider using models and tools designed specifically for code generation.

GitHub Copilot

It an AI code generator, can generate code based on various programming languages and frameworks. GitHub Copilot is powered by OpenAI Codex, which is a generative, pre trained language model. It helps developers generate solution based code. Copilot is trained on natural language text and source code from publicly available sources, including GitHub repositories. Copilot can be integrated as an extension with popular code editors, such as Visual Studio. It can produce code snippets that adhere to best practices and industry standards.

PolyCoder

It is an open-source AI code generator. It's based on GPT when trained on data from various GitHub repositories written in 12 programming languages. It's particularly accurate for writing C codes. PolyCoder offers an extensive library of predefined templates that can be used as blueprints for code generation for various use cases. PolyCoder can help create, review, and refine code snippets precisely customized to requirements.

IBM Code Assistant

Different code generators offer specific functionality and features. However, when the requirement is to enable hybrid cloud developers to write code for diverse needs, IBM Watson Code Assistant is an option. It's built on IBM watsonx.ai foundation models for developers of any skill level. You can integrate Watson Code Assistant with a code editor. It enables developers to write code accurately and efficiently with real-time recommendations, autocomplete features, and code restructuring assistance. Moreover, you can input code or project files into Watson Code Assistant for analysis. It identifies patterns, suggests improvements, and generates code snippets or templates. Developers can customize this generated code for specific project needs.

• Other Code Generators

Many other AI driven code generators and code assistant tools are available to help developers write accurate code faster. Amazon CodeWhisperer can be integrated with code editors and offers real-time code recommendations. Tabnine can be helpful for accurate code completions. Replit is a platform that provides an interactive space for users to code, learn, and collaborate.

With automatic code writing and optimization capabilities, AI based code generators help developers increase their productivity and quality of code. They enable rapid prototyping to iterate on design ideas. These tools also help with cross-platform compatibility and migration by enabling multilanguage code translation. AI based code generators follow consistent patterns and coding standards. They can suggest refactoring patterns to adhere to best practices. However, these tools should be used with caution to ensure that the AI generated code does not lead to ethical issues. For example, security vulnerabilities, as these tools may be used for generating malicious data or data biases based on training.

• Outline

In this Aricle, you learned that generative AI based models and tools can generate new code from text and image prompts, optimize existing code, and produce solution based code. ChatGPT and Bard are useful for simple code generation, debugging, and learning programming. Prominent code generators like GitHub Copilot, PolyCoder, and IBM Watson Code Assistant offer varied features such as real-time recommendations, code restructuring, and solution templates. In general, code generators improve productivity, accelerate development cycles, promote coding best practices, and foster consistent coding standards.

Taught by: Rav Ahuja, Global Program Director

IBM Skills Network