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  3. GitHub Copilot
Logo of GitHub Copilot

GitHub Copilot

byGitHub
in
4.4
Market Presence: Enterprise AI Coding Agents, Generative AI Knowledge Management Apps/General Productivity

Overview

Product Information on GitHub Copilot

Updated 13th October 2025

What is GitHub Copilot?

GitHub Copilot is a software developed to assist programmers by providing automated code suggestions and completions within integrated development environments. The software leverages machine learning models to analyze context from code inputs and generate relevant code snippets, reducing manual coding time and effort. It supports various programming languages and helps users discover suitable functions, algorithms, and implementation patterns. GitHub Copilot is designed to streamline software development workflows by improving coding productivity and minimizing repetitive programming tasks. Users can integrate the software with compatible editors to enhance code authoring efficiency and address challenges related to code generation and exploration.

GitHub Copilot Pricing

GitHub Copilot software uses a subscription-based pricing model, offering access for individual users and organizations with monthly or annual payment options. Pricing varies depending on whether the subscription is purchased for personal use or within a business context, with separate rates for individuals and enterprise-level features.

Overall experience with GitHub Copilot

Software Engineer
250M - 500M USD, Software
FAVORABLE

“Recent Updates Streamline Automated Testing and Framework Development With GitHub Copilot”

5.0
Jan 20, 2026
This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions.
Engineer
1B - 3B USD, Retail
CRITICAL

“Github Integration Enhances Code Review While Data Analysis Accuracy Remains Inconsistent”

3.0
Jan 30, 2026
This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions.

About Company

Company Description

Updated 14th January 2025

GitHub is a platform where developers, businesses, and organizations collaborate to create and innovate. Offering tools for version control, CI/CD, security, and code review, GitHub helps teams build software efficiently and securely. With GitHub Copilot, developers can leverage AI to receive real-time coding assistance, streamlining their workflows and enabling them to focus on solving complex challenges. The platform supports a wide range of projects, from open source to enterprise, while integrating seamlessly into development processes to foster collaboration and security. As part of Microsoft, GitHub is committed to empowering developers and organizations to bring their ideas to life, working toward the goal of supporting 1 billion developers worldwide.

Company Details

Updated 26th February 2025
Company type
Public
Year Founded
2008
Head office location
San Francisco, United States
Number of employees
2500 - 4999
Parent Company
Microsoft
Annual Revenue
1B-3B USD
Website
https://github.com

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Key Insights

A Snapshot of What Matters - Based on Validated User Reviews

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Peer Discussions

What Your Peers Are Saying About GitHub Copilot

VP of IT
What is the level of accuracy of GitHub copilot as code quality tool according to the following parameters? a. Syntax and Correctness b. Code Quality and Best Practices c. Security d. Context Awareness e. Refactoring and Optimization f. Language-Specific Accuracy
CEO
I recognize I may not be addressing the question directly, but even if large language models (LLMs) like GitHub Copilot are not yet at the level required for complete autonomy, isn't it reasonable to anticipate that, over time, their ability to learn patterns from our codebase will evolve to a point where human intervention becomes significantly less frequent? With this in mind, I believe the focus should be on preparing for that future—gradually adopting copilots and leveraging empirical data to guide their implementation and integration into workflows.
See Full Discussion
14 Dec 20242.7k Views2 Comments
Group Solutions Leader
How are large global companies implementing gen AI solutions as part of their larger AI strategy? As an example, there are three primary approaches, as per McKinsey: In “Taker” use cases, companies use off-the-shelf, gen AI–powered software from third-party vendors such as GitHub Copilot or Salesforce Einstein to achieve the goals of the use case. In “Shaper” use cases, companies integrate bespoke gen AI capabilities by engineering prompts, data sets, and connections to internal systems to achieve the goals of the use case. In “Maker” use cases, companies create their own LLMs by building large data sets to pre-train models from scratch. Examples include OpenAI, Anthropic, Cohere, and Mistral AI.
COO
From discussion with peers in banking and financial services, the 'taker' model is popular to 'dip their toes' into the Gen AI solutions. Often run as pilots or from existing partnerships with say Microsoft, this provides a low risk entry point into Gen AI. 
See Full Discussion
24 May 2024399 Views1 Comment

GitHub Copilot Reviews and Ratings

4.4

(462 Ratings)

Rating Distribution

5 Star
47%
4 Star
46%
3 Star
7%
2 Star
0%
1 Star
0%
Why ratings and reviews count differ?
  • Software Engineer
    50M-1B USD
    Software
    Review Source

    Recent Updates Streamline Automated Testing and Framework Development With GitHub Copilot

    5.0
    Jan 20, 2026
    Using GitHub Copilot has become a daily habit for my Selenium/java work. It's not just a fancy autocomplete anymore, the recent updates in have turned it into a proper coding partner. Having our automated test framework on GitHub makes the integration seamless. I use it mostly for boilerplate code, like generating those repetitive page object methods or writing snippets for our api tests. It's incredibly fast at picking up the patterns in the codebase, so it knows exactly how to name the variables and structure the tests without me having to explain it every time. The Copilot agent integration is the real game changer here. Unlike the standard suggestions, the agent can now look at my entire workspace and handle more complex tasks like refactoring an entire test suite or fixing a tricky race condition in our selenium scripts. I can just @workspace in the chat and ask it to find all tests that don't have proper teardown logic and it highlights them and offers a fix in seconds. It's also great for generating documentation for our test cases, which is something I used to spend way too much time on. I use copilot when I want to stay in the flow and write the code myself but with a lot of help. I use it for quick fixes and real-time suggestions, whereas I send the bigger, more time-consuming tasks over to other AI tools that I also use along with Copilot. Together, they make a powerful team. Copilot helps me with the logic and small details, and the other one handles the heavy lifting of building entire scenarios from scratch. It still has those moments where it suggests code that looks right but is actually slightly outdated it doesn't perfectly match our custom framework wrappers. Also, if I am working on a very large file, it can sometimes lose the context of what I was doing three hundred lines up. And just like with other tools, there's always that slight concern about it being a bit hallucinatory with library versions if it has not seen the latest updates yet. But honestly, for the speed and accuracy it provides in my daily Java development, it's hard to imagine going back to coding without it.
  • Software Engineer
    50M-1B USD
    Software
    Review Source

    Recent Updates Streamline Automated Testing and Framework Development With GitHub Copilot

    5.0
    Jan 20, 2026
    Using GitHub Copilot has become a daily habit for my Selenium/java work. It's not just a fancy autocomplete anymore, the recent updates in have turned it into a proper coding partner. Having our automated test framework on GitHub makes the integration seamless. I use it mostly for boilerplate code, like generating those repetitive page object methods or writing snippets for our api tests. It's incredibly fast at picking up the patterns in the codebase, so it knows exactly how to name the variables and structure the tests without me having to explain it every time. The Copilot agent integration is the real game changer here. Unlike the standard suggestions, the agent can now look at my entire workspace and handle more complex tasks like refactoring an entire test suite or fixing a tricky race condition in our selenium scripts. I can just @workspace in the chat and ask it to find all tests that don't have proper teardown logic and it highlights them and offers a fix in seconds. It's also great for generating documentation for our test cases, which is something I used to spend way too much time on. I use copilot when I want to stay in the flow and write the code myself but with a lot of help. I use it for quick fixes and real-time suggestions, whereas I send the bigger, more time-consuming tasks over to other AI tools that I also use along with Copilot. Together, they make a powerful team. Copilot helps me with the logic and small details, and the other one handles the heavy lifting of building entire scenarios from scratch. It still has those moments where it suggests code that looks right but is actually slightly outdated it doesn't perfectly match our custom framework wrappers. Also, if I am working on a very large file, it can sometimes lose the context of what I was doing three hundred lines up. And just like with other tools, there's always that slight concern about it being a bit hallucinatory with library versions if it has not seen the latest updates yet. But honestly, for the speed and accuracy it provides in my daily Java development, it's hard to imagine going back to coding without it.
  • Read All 467 Reviews

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User Sentiment About GitHub Copilot
Reviewer Insights for: GitHub Copilot
Deciding Factors: GitHub Copilot Vs. Market Average

GitHub Copilot Likes & Dislikes

Like

This tool is the absolute best part for me. It's like having a junior developer right there in the chat who can look at the whole workspace. If I hit a weird error in one of my Java files, I can just ask the agent to fix this bug in the current context, and it analyses the surrounding code to suggest the proper fix. It's also a huge help with writing unit tests for our helper classes, something I used to find really tedious but now takes just a few seconds. Another thing I love is the natural language to code feature. I can just write a prompt like 'check if the API response contains the user_id and status is 200' and copilot generates the entire validation block for me. It's not just a time saver, it also actually helps me write cleaner, more standard code. For someone with 8 years of experience, it's refreshing to have a tool that actually feels like it's making me faster without getting in the way of my own logic. It also feels like Copilot already knows my coding style and our specific project structure. It is amazing at predicting the next few lines of code when I'm building out a new test. I don't have to constantly look up syntax or method signatures anymore as copilot just offers them up in real time.

Like

This tool is the absolute best part for me. It's like having a junior developer right there in the chat who can look at the whole workspace. If I hit a weird error in one of my Java files, I can just ask the agent to fix this bug in the current context, and it analyses the surrounding code to suggest the proper fix. It's also a huge help with writing unit tests for our helper classes, something I used to find really tedious but now takes just a few seconds. Another thing I love is the natural language to code feature. I can just write a prompt like 'check if the API response contains the user_id and status is 200' and copilot generates the entire validation block for me. It's not just a time saver, it also actually helps me write cleaner, more standard code. For someone with 8 years of experience, it's refreshing to have a tool that actually feels like it's making me faster without getting in the way of my own logic. It also feels like Copilot already knows my coding style and our specific project structure. It is amazing at predicting the next few lines of code when I'm building out a new test. I don't have to constantly look up syntax or method signatures anymore as copilot just offers them up in real time.

Like

This tool is the absolute best part for me. It's like having a junior developer right there in the chat who can look at the whole workspace. If I hit a weird error in one of my Java files, I can just ask the agent to fix this bug in the current context, and it analyses the surrounding code to suggest the proper fix. It's also a huge help with writing unit tests for our helper classes, something I used to find really tedious but now takes just a few seconds. Another thing I love is the natural language to code feature. I can just write a prompt like 'check if the API response contains the user_id and status is 200' and copilot generates the entire validation block for me. It's not just a time saver, it also actually helps me write cleaner, more standard code. For someone with 8 years of experience, it's refreshing to have a tool that actually feels like it's making me faster without getting in the way of my own logic. It also feels like Copilot already knows my coding style and our specific project structure. It is amazing at predicting the next few lines of code when I'm building out a new test. I don't have to constantly look up syntax or method signatures anymore as copilot just offers them up in real time.

Dislike

It occasionally generates incorrect and outdated code and occasionally hallucinates by suggesting methods/classes within the libraries that don't exist. If you give it a raw data file its data analysis is mostly inaccurate, although if you ask it to write code to analyse the data it gets that right.

Dislike

It occasionally generates incorrect and outdated code and occasionally hallucinates by suggesting methods/classes within the libraries that don't exist. If you give it a raw data file its data analysis is mostly inaccurate, although if you ask it to write code to analyse the data it gets that right.

Dislike

It occasionally generates incorrect and outdated code and occasionally hallucinates by suggesting methods/classes within the libraries that don't exist. If you give it a raw data file its data analysis is mostly inaccurate, although if you ask it to write code to analyse the data it gets that right.