GitHub CoPilot Reviews

The primary focus has been on leveraging its capabilities for increased ease of use and productivity. The main goal is to accelerate application development, allowing us to build applications more efficiently.

The most valuable aspects in terms of features, revolve around its code generation capabilities. Firstly, the functionality for generating test cases based on existing code and application logic has been invaluable. Secondly, the code explanation feature has emerged as another essential tool. Beyond these, the primary function of facilitating the development of new code or additions to existing code remains a key focus.

There's room for improvement to ensure that suggestions align more precisely with the context of what I'm seeking, minimizing instances of unrelated or inaccurate code suggestions. It could benefit from enhancements in several key areas. Firstly, there's a need for improved integration across different Integrated Development Environments to ensure a consistent user experience. Secondly, providing users with more granular control over when Copilot is active, preferably on a per-project basis, would enhance flexibility. Also, broadening Copilot's contextual awareness beyond open files to consider the entire project or codebase context could lead to more accurate and relevant suggestions, particularly in scenarios where opening all files is impractical. The current state lacks maturity, and reporting is not readily available out of the box. Users have to request specific reports, involving collaboration with the data team, indicating a need for more streamlined and accessible reporting features.

I experimented with the technical preview version when it was initially launched last year, taking advantage of the free access provided at that time. However, in a professional context, I have been utilizing it for real-time database work over the past three and a half months.

The stability is generally satisfactory. The tool itself operates smoothly, but potential challenges may arise, especially in larger enterprises utilizing proxies. It's crucial to adhere to the documentation on operating with proxies to avoid glitches.

The scalability is impressive. It accommodates growth seamlessly; as you acquire more licenses, the tool scales accordingly to meet the increasing demand. As of now, we have successfully onboarded and currently have approximately three hundred and fifty active users.

We have engaged with the support team for feature requests, particularly in terms of porting, and have found the response to be somewhat delayed. It's worth noting that GitHub and Microsoft teams have communicated that they are actively addressing these concerns in private.

The initial setup is straightforward.

The initial setup operates on a license-based model, providing flexibility as you can allocate licenses to a GitHub team. While the implementation itself is straightforward and requires minimal ongoing maintenance, a significant challenge has emerged in terms of documentation, especially concerning security documentation. The current lack of comprehensive documentation raises concerns, particularly for larger enterprises. When activating Copilot, you are prompted to provide essential information, such as the location of your Azure instance where suggestions will be processed. Once you input this information, the installation proceeds seamlessly.

While it's premature for a comprehensive ROI analysis, we have observed a positive trend with users expressing satisfaction.

A personal license is priced at ten dollars per month, while a professional or enterprise license costs nineteen dollars per user, and these rates are consistent for all users.

While we have explored various options within the Microsoft ecosystem, such as the solution offered, we haven't actively employed any other AI tools at the enterprise level.

It's essential to perceive it as a supplementary tool, not the primary driver. Caution should be exercised in blindly accepting suggestions to avoid unexpected challenges. The time invested in reviewing suggestions is crucial, and the tool's effectiveness depends on the user's domain knowledge. While it can be ideal for bridging domain and technical aspects for those with expertise, users with less experience may overlook potential issues, necessitating careful consideration. Overall, I would rate it seven out of ten.

I use the solution in my company for information about the user story. It also helps to know how many tests or flows we need to test.

The most valuable skill and characteristic of GitHub CoPilot is that you can find any information in seconds.

The technology offered by the product in the retail industry and banking processes has certain shortcomings, making them areas that can be improved.

I have been using GitHub CoPilot for five months.

It is a scalable solution. Scalability-wise, I rate the solution a seven out of ten.

I think the use, setup, or maybe how we can use the tool depends on how much of your tasks can be done with it. For example, if you can use the tool every day for less than an hour, you can improve your tasks and skills with the tool on a daily basis. In my case, for example, I use Postman every day so I can improve my job and my tasks every day.

I rate the technical support a nine out of ten.

Positive

I have experience with ChatGPT and Vertex AI from Google. I think they are all the same tools coming from different providers. The principal difference between all the tools is the updates. Vertex AI has information from the last year, but GitHub CoPilot may have information for the current year. It all depends on how many updates one can have on a product.

The product's initial setup phase is easy.

The solution can be deployed in a matter of minutes.

The tool saves 25 percent of the time for me.

Speaking about the tool's ability to handle complex coding tasks compared to simpler ones, I would say that you can take care of the flows in Postman and translate them using Selenium or Python, and you can add it to your automation test.

GitHub CoPilot has adapted to the specific languages and frameworks I use, and it offers great assistance. I can say this because if I have one question, I can go to GitHub CoPilot to ask my question, and I find the information I need. I think GitHub Copilot is of great assistance as it can add more information about your work, your job, your task, and your flows, and you can find better answers and better examples day by day.

I rate the tool a nine out of ten.

GitHub CoPilot helps its users summarize documents. GitHub CoPilot has an AI capability that allows it to give its users a summary of documents that it has received from them. GitHub CoPilot also provides meeting summaries and suggestions on how and when you are supposed to send reports or emails. GitHub CoPilot establishes a total baseline of its users' activities so that it can later recommend to its users how to structure their day.

GitHub CoPilot's stability is an area of concern where improvements are required.

I have been using GitHub CoPilot for three or four months. I have experience with the preview version of the product. Though I am not sure about it, I think that GitHub CoPilot is not yet released in the market.

Stability-wise, I rate the solution a seven out of ten.

I will not be able to comment on the scalability part of the solution since my company has not undertaken a full-fledged deployment process across our organization's users.

All the people in my company use GitHub CoPilot in one form or the other.

The product's initial setup is straightforward because it's pushed through the updates. The product's updates are allowed automatically, so the setup process is not something one needs to get involved in deeply.

The solution can deployed in less than a minute since it is done through the automatic updates provided by GitHub.

No technical people are required to take care of the deployment and maintenance of the solution since it gets automatically pushed as an update from GitHub CoPilot.

The solution's deployment part was managed with the help of one of our company's in-house teams.

GitHub CoPilot comes readily available for enterprise customers, so it's a free add-on if you already have GitHub's enterprise license.

I recommend the solution to those who plan to use it.

As it is a new product in the market, I rate the overall tool an eight to nine out of ten.

We use GitHub CoPilot for syntax purposes. It helps me a lot when I forget my mistakes.

The solution's most valuable features are context awareness, multi language support, integration with popular IDs like Visual Studio Code or JetBrains, and reduced coding time.

The solution provides good code completion and code generation. We have a learning aid in the framework for learning purposes. For prototyping, the tool has prototyping ideas in the core structures, which is very helpful. The solution helps me with testing and debugging.

Multi-language support is also available in GitHub CoPilot. Visual Studio Code or JetBrains can be seamlessly integrated to work with CoPilot, which is helpful. The code quality of the solution will help me reduce my coding time when we do complex things. Overall reliance on GitHub CoPilot reduced the manual coding skills. All those things will help me a lot while using GitHub CoPilot.

The solution's accuracy and relevance could be improved. My application may not be very accurate, but that is one reason we should carefully review and modify the code. If you take the code generated by the tool bluntly, it will give you a different kind of error. The solution's dependency on AI reduces manual coding skills.

Regarding licensing and copyright issues, we should be cautious about the solution's potential to embed code snippets that are very similar to our copyrighted code. We cannot take any chances with copyright issues.

I have been using GitHub CoPilot for the last one year.

I rate the solution’s stability a seven out of ten.

I rate the solution’s scalability an eight out of ten.

The solution’s initial setup is easy.

The solution's installation may take around 20 minutes.

GitHub CoPilot is less expensive than other solutions.

GitHub CoPilot does not require much maintenance. The solution has reduced our coding time and manual work by 10% to 30%.

Overall, I rate the solution a seven out of ten.

Apart from being a DevOps and solution architect, I am also a data analyst. I use GitHub CoPilot when I want to do some analysis and get some exact technical details. I don't have time to always go to my browser and figure out how to implement something, so once I am at work and I have the initial setup done already, I really know what to do and how to go about it. The product is simple to work on since if I have to work on a project, I can do some analysis before developing infographics, making it a really efficient product.

Some of the suggestions provided by GitHub CoPilot are not accurate, making it an area of concern where improvements are required.

I have been using GitHub CoPilot for two years. I use the solution's latest version. I am a customer of GitHub.

Stability-wise, I rate the solution an eight out of ten.

The stability issues in GitHub CoPilot are the same as the ones in GitHub Codespaces. Even with a high-speed internet connection, the product hangs and has some glitches.

Scalability-wise, I rate the solution an eight out of ten since the product's scalability still has a lot of room for improvement.

I haven't contacted the technical support team of GitHub CoPilot since if I face any stability-related issues with the product, I stop or disable it and then enable it, after which it starts working again.

The initial setup of the product is easy.

For deployment, you just need to log in to your GitHub account through your ID, after which GitHub Codespaces automatically connect to GitHub CoPilot to your GitHub account.

The solution is deployed on the cloud.

GitHub CoPilot's deployment process did not take a long time.GitHub CoPilot is a service that is not complex. The deployment process was very fast.

The product offers a free version and a paid version. Whether to choose the product's free version or paid version depends on the size of the company where it will be used. If your company size is going to increase and many people plan to use the product, then GitHub will charge you a certain amount. The account on GitHub I use is free for a year, after which I need to start paying if I want to use the solution. In my company, for some of the users, I have seen that certain payments need to be made to GitHub CoPilot.

Before using GitHub CoPilot, you need to consider what you are working on so that you don't miss what you are trying to achieve. When you are working on professional projects or very important projects for which, it may not be a good idea to use GitHub CoPilot. You should have good knowledge of what you are doing.

I rate the overall product an eight out of ten.

We use the tool for code snippets that we integrate into our solutions. 

GitHub CoPilot helps us auto-complete code. The tool's context awareness benefits our projects because it identifies the context in which we are working and automatically proposes solutions that we use. 

The most important factor is that we need less time for design. We can cover solutions more quickly; our updates will be within days, not months, as before.

The tool needs to focus on integration, as it is the most important aspect. I would like to see some pre-designed modules included in my projects.

I have been using the tool for a month. 

I rate the solution's stability a nine out of ten. It is good and promising. 

GitHub CoPilot's scalability is quite satisfactory. I rate it a ten out of ten. My company has two users. 

We use support from Microsoft's online documentation and resources. We don't need to contact human support.

GitHub CoPilot's deployment is straightforward. It is used as a tool for assistance rather than being deployed within our projects.

We have a demo license. Once we understand what we'll do, we'll start with a paid license.

We are still in the planning phase. We have attended some seminars by Microsoft to understand our needs better. Once we have a clear plan and strategy, we will discuss whether we need professional help. 

My advice for people who want to start using it is to understand how it works and to find uses for the platform. This means focusing on how to develop their processes, not traditionally, but by utilizing these AI tools.

I rate the overall solution a ten out of ten. 

The product's most valuable feature is vulnerability management features.

GitHub CoPilot's alerting features need improvement.

We have been using GitHub CoPilot for 3 years.

The product has high stability.

We haven't encountered scalability issues as we are still in the process of onboarding more applications. However, it might be beneficial once we transition to the cloud.

Around 50 developers from our organization use GitHub CoPilot daily. However, most developers still use Azure DevOps.

We used Azure DevOps. We decided to switch to CoPilot for advanced security features.

The initial setup is easy. It takes a few days to complete. I rate the process a 9 out of 10.

Our in-house team of developers implemented the platform. At the moment, a team of 30 developers is maintaining it.

GitHub CoPilot has greatly enhanced coding efficiency for new projects, particularly through its advanced security features.

The platform offers a comprehensive package, including firewall capabilities. You don't need to download additional applications or purchase separate solutions for security testing and vulnerability management.

I rate it a 9 out of 10.

For example, I participate in a radio show on IT topics. I use OpenAI to prepare the script I will use in the program. So, I need some queries and questions or topics for discussion. I use OpenAI to create AI business scripts tailored to how I want to address the topics. I have used OpenAI to improve my English skills. For example, I have asked for recommendations to enhance this text.

I have used OpenAI to learn how to engage with my clients, daughters, and nephews. For example, they gave me a challenge where we discussed a topic and started asking OpenAI questions to see what responses it generated. On the professional side, I have primarily used it for improving emails, resumes, or other situations. Also, in coding, I remember using it when encountering issues implementing code for an application. I usually start by asking OpenAI before turning to Google or other platforms because it helps me identify the problem faster in specific situations.

In certain instances, OpenAI didn't respond in the expected way. It was in the Power Automate. The responses were more general and didn't address the specific point. There were significant limitations. Typically, the queries I've used it for are relatively small in scope.

I prefer OpenAI over Copilot, which is integrated with coding tools like GitHub, because Copilot seems difficult to use. Copilot's responses are different from what I need to understand the problem. OpenAI has helped me a lot. It hasn't provided direct solutions, but it aids in pinpointing the issue.

Initially, OpenAI is free, but you'll need to pay for it later.

Overall, I rate the solution a 9 out of 10.