GitHub Copilot
Hey Wanna Deliver Project Code Fast??
Here I Am….
Your Copilot…
Introduction
Large Language Models (LLMs) and Machine Learning for autocompleting source code are becoming more and more popular in software development. LLMs have powerful natural language processing (NLP) capabilities nowadays.
Machine Learning approaches have been widely used by being applied to source code text in a variety of new tools to support software development, which makes it possible to use LLMs to synthesize code in general-purpose languages.
With the advances in Machine Learning, there is a growing interest in AI-enabled tools for autocompleting source code. GitHub Copilot, also referred to as the “AI Pair Programmer”, has trained on billions of lines of open-source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021.
GitHub Copilot is a software development tool that offers code generation of lines, code chunks, or even entire programs based on existing code and comments.
Pair Programming as a Software Development Practice
GitHub Copilot is marketed as a substitute for pair-programming, a software development practice where two programmers collaboratively write a single piece of code. GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. It draws context from comments and code to suggest individual lines and whole functions instantly.
GPT-4 as a Building Block
GitHub Copilot is powered by OpenAI Codex, a generative pretrained language model created by OpenAI. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub.
The Tech Stack with Copilot
Popular programming languages, IDEs, and technologies have been recognized as being utilized with Copilot. The results or observations from practitioners using Copilot are:
(1) The major programming languages used with Copilot are JavaScript and Python. Besides, developers often write C#, TypeScript Rust, PHP, C, Golang, and Kotlin were used with Copilot.
(2) The main IDE used with Copilot is Visual Studio Code. Others include Vim, Neovim and the JetBrains suite of IDEs.
(3) The most common used technology with Copilot is Node.js. In addition, .NET which works for Web development, and Vue, React, and Ajax which are frameworks for front-end development, were mentioned less often compared to Node.js in usage.
(4) The leading function implemented by Copilot is data processing. When implementing functions, developers also use Copilot to code image processing, algorithm, iteration, calculation, filtering, printing, memory read, serialization, and URL building.
(5) The significant benefit of using Copilot is useful Code generation, Faster development and Better user experience.
(6) The main limitation encountered by practitioners when using Copilot is difficulty of integration.
The Different visual studio extensions available with copilot are:
è Copilot - AI pair programmer with in-IDE code suggestions
è Copilot Nightly - Nightly build of Copilot, includes latest changes.
è Copilot Labs - Experimental features in sidebar
è Copilot Chat - Interactive chat in sidebar, part of Copilot X
è Copilot Voice - Voice assistant
*The above extensions will likely change as GitHub evolves these products.
How Does the Customer get the most out of Github Copilot
GitHub Copilot is trained on public code. When a new library, framework, or API is released, there is less public code available for the model to learn from. That reduces GitHub Copilot’s ability to provide suggestions for the new codebase.
As more examples enter the public space, we integrate them into the training set and suggestion relevance improves.
GitHub Copilot works best when you divide your code into small functions, use meaningful names for functions parameters, and write good docstrings and comments as you go. It also seems to do best when it’s helping you navigate unfamiliar libraries or frameworks.
Use Cases for GitHub Copilot
The uses cases for GitHub Copilot are numerous, especially when you add in the preview features of Labs, Chat, and Voice.
Using Copilot's features can really streamline the development process on the use cases like Code Generation, Explaining Code, Language Translation, Debugging, Refactoring, Test Generation, Code Reviews and Voice-Driven Development.
Limitations and Challenges with GitHub Copilot
Copilot is designed to generate the best code possible given the context it has access to, but it doesn’t test the code it suggests so the code may not always work, or even make sense.
GitHub Copilot can only hold a very limited context, so it may not make use of helpful functions defined elsewhere in your project or even in the same file. And it may suggest old or deprecated uses of libraries and languages.
When converting comments written in non-English to code, there may be performance disparities when compared to English. Other limitations and challenges are Difficulty of integration and High pricing.
About billing for GitHub Copilot
GitHub Copilot is a paid feature, requiring a monthly or yearly subscription. GitHub Copilot subscriptions can be paid for and managed through a personal account on GitHub.com with Copilot for Individuals or paid for and managed centrally through an organization account with GitHub Copilot for Business.
| Copilot for Individuals | Copilot for Business |
Pricing | $10 per month/$100 per year | $19 per user per month |
Types of GitHub accounts | Personal accounts | Organization or enterprise accounts |
Telemetry | ü | û |
Blocks suggestions matching public code | ü | ü |
Plugs right into your editor | ü | ü |
Offers multi-line function suggestions | ü | ü |
Organization-wide policy management | û | ü |
VPN Proxy support via self-signed certificates | û | ü |
