How Developers Can Leverage AI Tools for Programming?

    How Developers Can Leverage AI Tools for Programming

    Coders are incorporating AI into their workflow, resulting in a substantial increase in coding productivity. AI can generate complete code, translate, debug, and improve the quality.

    AI is advancing programming as the technology speeds up delivering better code and eases the complexities. Many coders are incorporating AI into their workflow as they realize the effectiveness of AI in programming.

    The effectiveness of the technology is seen in generating programs, translating complex codes, and improving the quality simultaneously. AI-generated code is constantly improving and helping coders deliver accurate codes quickly.

    One of the most recent breakthroughs in NLP is the development of large language models (LLMs) built from massive datasets containing countless individual data points. Many LLMs are available, including OpenAI’s GPT-2, GPT -3, and Google’s BERT. These models can generate anything from straightforward essays to legitimate financial models. By training models with countless parameters, AI startups like OpenAI, Hugging Face, Cohere, and AI21 Labs are pushing the limits of LLM.

    Here are a few AI-based code generators that can produce high-quality code based on large language models.

    AI Codex Open                  

    GitHub Copilot generates code in common development environments such as VS Code, JetBrains, Neovim, and even in the cloud with GitHub Codespaces. They function in support of OpenAI Codex, a GPT-3-based model. It supports 12 programming languages, such as BASH, Go, JavaScript, Perl, Ruby, PHP, Swift, and TypeScript.

    The model is trained on trillions of lines of publicly accessible code from places like GitHub repositories. Through a private beta, OpenAI has made the model accessible to platform providers and developers to create tools and integration.


    Researchers at SalesForce created the open-source programming language model known as CodeT5, built on the foundation of the T5 (Text-to-Text Transfer Transformer) framework from Google. The team used over 8.35 million instances of code, along with user comments, from openly available GitHub repositories to train CodeT5. Most of these datasets were derived from the CodeSearchNet dataset, including two C and C# datasets from BigQuery and Ruby, JavaScript, Go, Python, PHP, and C and C#.

    The ability of CodeT5 can make three potential improvements to software programming such as:

    • Text-to-code generation: It creates code-based natural language
    • Completes the entire code function using the target function name in code auto-completion
    • Create a function summary in natural language using code summarization


    The Polycoder is OpenAI’s answer to the Codex. CMU researchers created their model based on OpenAI’s GPT-2, trained with a 249 GB codebase in 12 programming languages. PolyCoder tool is more accurate than Codex when writing C code. Polycoder is among the earliest open-source code generation models among other closed-source code generators.


    Tabnine improves the IDE’s auto-completion capabilities. Rust student at the University of Waterloo, Jacob Jackson, created Tabnine, which is now a fully functional AI-based code completion tool. Tabnine is compatible with a wide variety of programming languages and editors, including 20 altogether.


    Berlin’s Y-Combinator company Cogram uses Jupyter Notebooks and SQL queries to generate code for data scientists and Python developers. Data scientists can use the tool to write queries in English, which will then be translated into complex SQL queries. You can use it with databases like Amazon Redshift, MySQL, or SQLite.


    DeepCode is an AI-driven code review tool that analyzes code and suggests changes. Lining, code completion, and refactoring are just some of its features. Open-source projects can use DeepCode for free, while commercial enterprises can upgrade to a paid plan.


    Kite is an open-source, AI-driven Python code completion tool. Software developers can benefit from machine learning in a number of ways. Kite provides access to premium services such as in-depth code analysis and refactoring utilities. Kite stands out from the rest of the pack because it is compatible with more than 16 languages and 16 code editors. Kite is regularly updated, making it more reliable and effective than competing machine-learning code tools.


    When it comes to open-source AI code completion tools, Jedi is another option. It is designed primarily as an add-on for IDEs and editors that support Python static analysis. While its primary purpose is code auto-completion, it also makes use of other features like goto, search/find, and refactoring. Its simple API makes it useful for a wide range of editors and projects, such as Visual Studio Code, Sublime Text, Vim, Emacs, Kate (4.13 and later), Atom, GNOME Builder, Gedit, and IPython.

    Also Read: Code Crafting in the AI Era: Leveraging Generative Models for Superior Development

    Wind Pro

    The editor analyzes static and dynamic code to offer superb, situation-specific recommendations. It also provides an intelligent error-checking tool for a better editing experience. The built-in Python shells and the auto-completion feature of the editor are available. This tool has a Source Assistant that continuously updates to offer call suggestions, type information, and inline documentation. It also automatically inserts function and method parameters as coders write code.

    In addition, there are a few more tools programmers, and developers can find helpful. These include programs like DeepCode, AIXcoder, and CodeWP and to name a few. These AI coding tools support various coding languages, offer partial and full code completion, and integrate with GitHub libraries.


    Please enter your comment!
    Please enter your name here