6 AI Models/Tools for Code Generation

In the dynamic world of software development, a trend is emerging, promising to reshape the way code is written—text-to-code AI models. These innovative models leverage the power of machine learning to generate code snippets and even entire functions based on natural language descriptions.

Imagine a future where developers can simply articulate their programming needs in plain English, and AI systems translate those words into functional code. This article will help you learn about the different AI models used for generating codes.

Salesforce CodeGen

  • Salesforce CodeGen is a large-scale language model facilitating conversational AI programming.
  • It operates as an “AI pair programmer,” converting natural language descriptions into actual code.
  • CodeGen allows users to describe coding tasks to the machine instead of manually writing the code.
  • While enhancing efficiency for low-code professionals, it complements rather than replaces the need for developers, especially for more complex problems.


  • CodeGeeX is hosted on Hugging Face Spaces, a platform for creating and sharing machine learning applications.
  • Users can try CodeGeeX online, providing natural language queries and selecting the target programming language for code generation.
  • It is an open-source project with a GitHub repository, offering a versatile solution for code generation tasks.
  • CodeGeeX leverages state-of-the-art natural language processing and deep learning techniques, enhancing accuracy and robustness.


  • CodeBERT is a pre-trained model designed for programming languages, specifically trained on NL-PL pairs in six languages.
  • Code-to-Code Translation – facilitates code completion or translation between programming languages.
  • Code to Text – assists developers in summarizing unfamiliar code by translating it into natural language.
  • It provides a code search feature, allowing users to retrieve relevant code based on natural language queries.


  • Duckargs simplifies the creation of Python or C programs that accept command line arguments.
  • Users can run ‘duckargs’ (generates Python), ‘duckargs-python’ (also generates Python), or ‘duckargs-c’ (generates C) with desired options/arguments.
  • The tool automates the generation of code for handling specified options/arguments, reducing the need for manual argparse or getopt.h boilerplate.
  • Duckargs aims to streamline the process of creating quick command-line tools, minimizing the time between ideation and having a functional program.


  • CodeT5+ is an advanced open-code large language model introduced by Salesforce Research, designed for code understanding and generation.
  • Deployable as an AI-powered coding assistant, CodeT5+ enhances developer productivity by offering text-to-code generation, code autocompletion, and code summarization capabilities.
  • Built upon an encoder-decoder architecture, CodeT5+ leverages state-of-the-art natural language processing techniques, extending the capabilities of previous models like CodeT5 and CodeBERT.
  • CodeT5+ learns from extensive public code repositories, enabling it to comprehend and generate code in multiple programming languages. Developers can automate tasks, receive code suggestions, and expedite the coding process with intelligent assistance from CodeT5+.


  • GitHub Copilot provides autocomplete suggestions based on the context of your existing code, offering quick and context-aware code completion for individual lines or entire functions.
  • Users can prompt Copilot using everyday language, enabling code generation by describing the desired functionality in natural language, making it accessible to developers regardless of their native language.
  • Copilot’s “Explain the Code” feature helps developers understand existing code by providing explanations in plain language, facilitating collaboration, and easing comprehension of unfamiliar codebases.
  • With Copilot Labs, translation of code from one programming language to another is supported, offering a starting point for adapting code to different languages, though thorough validation is recommended.

Manya Goyal is an AI and Research consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Guru Gobind Singh Indraprastha University(Bhagwan Parshuram Institute of Technology). She is a Data Science enthusiast and has a keen interest in the scope of application of artificial intelligence in various fields. She is a podcaster on Spotify and is passionate about exploring.

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