Overview
Product Information on GitHub Copilot
What is GitHub Copilot?
GitHub Copilot Pricing
Overall experience with GitHub Copilot
“Recent Updates Streamline Automated Testing and Framework Development With GitHub Copilot”
“Github Integration Enhances Code Review While Data Analysis Accuracy Remains Inconsistent”
About Company
Company Description
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
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
User Sentiment About GitHub Copilot
Reviewer Insights for: GitHub Copilot
Deciding Factors: GitHub Copilot Vs. Market Average
GitHub Copilot Likes & Dislikes
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.
It's out of the box integration with Github web and VS code. Github web integration gets handy when you are reviewing a PR and want AI assistant's help understanding the bits. Explain and Ask about this diff provide clear descriptions that make the reviewer understand the complex logic/unfamiliar code. VS Code extension help increase productivity by providing contextually relevant suggestions.
The biggest strength of GitHub Copilot is its ability to accelerate development by reducing manual effort for common coding patterns and boilerplate tasks. It integrates seamlessly into developer workflows and supports multiple programming languages and frameworks. Real time code suggestions that improve development speed. Ability to generate functions, test scaffolding and documentation. Natural language prompts that help developers explore solutions quickly. Integration with IDEs and version control workflows. Helpful for learning new framework or APIs. Emerging capabilities that support more contextual and multi-step assistance. From a testing and developer perspective , it helps speed up unit test creation and improves productivity during early development phases.
The context window can be a bit of a frustration. In our larger automation files, especially those with hundreds of lines of api test cases, copilot sometimes loses track of the logic I established at the top of the file. It starts suggesting variable names or logic that doesn't align with the rest of the script, which means I have to stop and manually correct it. It's not a dealbreaker to be honest, but it does break the momentum.
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.
One limitation is that AI generated suggestions can sometimes lack full context awareness, especially in complex enterprise codebases or domain specific logic. Development still need to validate outputs carefully to ensure correctness and adherence to coding standards. Other considerations include :Potential inconsistency in suggestion quality depending on context. Limited understanding of proprietary business logic without additional context. Risk of over - reliance by less experienced developers. Security and compliance considerations when working with sensitive code. Agent style capabilities are still evolving and may not yet fully support complex and testing practices.
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Peer Discussions
What Your Peers Are Saying About GitHub Copilot
GitHub Copilot Reviews and Ratings
- Software Engineer50M-1B USDSoftwareReview Source
Recent Updates Streamline Automated Testing and Framework Development With GitHub Copilot
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. - Sdet10B+ USDBankingReview Source
AI-Assisted Coding Offers Speed Gains with Limitations in Context and Compliance
Overall, our experience with GitHub Copilot has been positive , particularly in improving developer productivity and reducing time spent on repetitive coding tasks. The tool has evolved beyond simple code completion into a more contextual AI assistant that can help with code generation, documentation, and basic troubleshooting, As it transitions toward more agent -like capabilities, Copilot shows potential to support more complex workflows such as multi-step code generation and assisted problem solving. However , it still requires careful human oversight , especially in enterprise environments where code quality, security , and compliance are critical. When used thoughtfully, it acts as a strong productivity enhancer rather than a replacement for developer expertise. - Manager, Project Management10B+ USDConsumer GoodsReview Source
Positive Copilot Adoption, but needs more enterprise flexibility and clearer metrics
OVerall, our experience with GitHub has been 4 out of 5. They are making a real effort to work with us, collaborate, and create good review channels. We appreciate their engagement and willingness to move things forward. However, at times it feels like our feedback is heard but not fully acted on. There is also a lack of clear commitment to timelines, which creates uncertainty for our team. Clearer follow through and more defined deadlines would strengthen the partnership. - DIRECTOR50M-1B USDBankingReview Source
Co-Pilot Simplifies Initial Setup but Misses Security and Best Practice Issues
Github co-pilot was my gateway into AI-assisted development. Co-pilot paved the way for its incumbents. And unfortunately, I found myself gravitating towards the latter for a few different reasons. Overall, great for getting folks who are starting to embrace AI tooling in their workspace, but starts to lose its value over time as one becomes a power user. - SENIOR SOFTWARE ENGINEER10B+ USDManufacturingReview Source
Github Copilot Agent Mode Enhances Workflow But Needs Multi-Agent Configuration
Github Copilot is a great tool that optimizes my daily work, automatizing tedious tasks and helping in some of the developments. Despite LLMs has still room for improvement and will require much more context to be able to work in large projects properly, we have take advantage of github copilot, especially the agent mode.