GitHub CoPilot Reviews

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.

Today, if you're aware of the CI/CD requirements, then it's fine. Unless you know how to create the pipeline, it would be tough. The one I'm using is already set. So I can implement it immediately. If there is a deviation from that, then it will be a little tough, I would need to Google to find the solution. I can implement it.

When you're using it, it's automatically deployed. In a normal application, it used to provide a single application. In this proprietary thing, it is just now it used to provide in the scripting tag, you need to provide Spring Boot.run, and that server code. So that unit is forwarded. In Copilot, you need to mention which nodes you are going to configure. So you need to provide all the port numbers and then deploy there. It will automatically deploy everywhere.

The deployment is easier when you're handling multiple deployments. So, I don't need to node application unit for node, update, and everything everywhere. But it's also in the engine we can do there. 

Once you set up everything, it is automatically handled. But once the setup is done, it's done.

GitHub Copilot code suggestion capabilities: It's always good. When you're doing any deployment, the suggestions are always welcome. You don't need to remember everything. You can pick from the solutions provided.

One thing I noticed is that normally, in CI/CD pipelines, we put: 

• the Jenkins path, 
• the Git repository paths, and 
• the number of reports we are going to deploy.

If we had an automatic system, an automatic page to do all these things, it would be easier. Not a configuration, but a page. If you have that kind of page, then Copilot can help all the developers. This is my individual preference. It can be there, it may not be. Maybe I'm not able to explore those issues, but that is one thing I noticed.

Currently, we need to map everything. We need to provide the Git card and where we need to deploy. If it is pre-prod, stage, or production, we need to map it separately. If we had some kind of program and could take it from the application of properties file, it would have been great. We configure everything in separate files. 

Even for development, staging, and production environments, we need to make separate files for each. If you have some mechanics to automate that, it would be great. It's for the entire system. Their system is like that. You need to prepare separately for every manage. So if you have one utility to make all those alternates, it would have been great. A UI would enhance the developer experience and streamline the CI/CD process.

You need to configure it separately for each environment. If everything is in a dropdown, it could be directly included. It will automatically connect to your dedicated agent. But it is okay. As of now, it's working fine. It's just manually maintaining that data. It worked fine. No issues. That is the only suggestion I have.

TL;DR:

For different environments, like production, stage, and development, if we can have a single point, like one, two, or three instead of opening for each, that feature would be helpful. We have numbers for certain regions, but again, the developer and the team should remember which number is for dev, stage, and prod. That could be easier for maintenance. 

The administrator doing the CI/CD pipeline should remember that number. It can be easier for any AI tool to do that. Instead of putting the phase in the configuration, if you put in a AI tool, it is easier for the system to manage.

I have been using it for three months. 

It's stable. 

It's scalable.

In the last team that we had around 13 members, everyone never faced any issues when using Git merge and Git commit. You have a separate Git repository, and you just need to know how to use it. The only thing is, our case of sessions should be organized, and the resources should be proper.

You want to see what everything the account does. I do everything with a command prompt, not using any Git GUI. You'll get the GUI application for your commit and such. It's fine.  

Deployment time: If the database scripting and the data center information are ready, then it will take one or two hours. It's a server configuration. Two to three hours of work.

Deployment resources: I can also do it. It doesn't require multiple people. If the software is ready and everything is fine, then only one person can do everything. That is what a full-stack developer means. If you are a full-stack developer, it means you can handle all the data-related tasks and services. I'm a full-stack developer, so I'm ready to do that. I should have the capability to do everything on the website.

We have a separate team to do that. They will do the things for us. From my side, I just raise a ticket in Jira support, and then they will take the ticket and implement it. The installation and configuration is a separate process because in each organization, they have separate monitoring systems.  

There's a separate team to make those things happen for me.

Overall, I would rate the solution a nine out of ten. The only suggestion is to enhance Copilot's ability to assist developers with infrastructure as code tasks.

Like, while using CI/CD pipeline, when I use YAML files, so it could just support the port number. That will improve the feature. But the developer should be aware of the feature.

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 platform to accelerate coding by providing relevant code suggestions and automating repetitive tasks. I use it in a development environment focused on various programming languages, where it helps by predicting code snippets and detecting variable names, which speeds up the coding process.

The platform's most valuable feature is detecting and suggesting variables based on the existing file context. This feature streamlines coding by offering relevant variables, making it easier and faster to write accurate code.

They could enhance the product's accuracy of suggestions, especially when dealing with non-standard or legacy code. Additionally, an easier and more intuitive way to toggle Copilot on and off would be beneficial.

I have been using GitHub Copilot for a few months.

The code prediction is generally stable, though there are occasional inaccuracies. 

The initial setup was very straightforward. 

The implementation was done in-house.

The return on investment may vary depending on the project. The predictions aren't always accurate for large enterprise solutions or extensive codebases. However, for standardized projects, it can significantly improve productivity.

GitHub Copilot is a paid service, costing approximately $6 per month. There might be a free trial available.

I decided to try GitHub Copilot because of its integration with GitHub and my existing workflow.

GitHub CoPilot is quite capable of AI. It provides good suggestions and aids in coding, but the increase in productivity is modest.

I recommend that others analyze if it works for their requirements. It's important to assess if it fits their workflow before fully integrating it into their coding practices.

I rate it a seven 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.

I'm using GitHub Copilot as a plugin for IntelliJ IDE. Specifically, I'm using the Community Edition of IntelliJ, not the commercial version. However, our organization provides us with a commercial license for GitHub Copilot.

I can upload code snippets or class files and ask for solutions to improve them, which works well. It's a great productivity tool that saves me time searching websites. While there are alternatives like ChatGPT and other AI tools, the advantage of the tool is that it is built into the IDE.

One drawback is that the solution sometimes suggests unwanted code, especially if I accidentally press the tab. This doesn't happen often. Sometimes it seems to understand my code, but other times it doesn't. This inconsistency is confusing.

I suggest adding a feature to write code directly in the IDE from chat responses instead of copying and pasting. GitHub Copilot can generate code based on method comments in the IDE, but chat responses require manual copying. A feature flag to allow direct writing from chat to IDE would streamline the process.

The product should also reduce its price so that it can improve the community engagement. 

I have been using the product for three months. 

As for performance and usage, I've only encountered connectivity issues once or twice. It responds well about 99 percent of the time. 

Regarding stability, there have been a couple of instances where the chat plugin got stuck. However, I don't see this as a major concern. It might improve over time, but it's not a significant issue.

The tool's deployment is mostly straightforward. The plugin installation is simple. However, there's one confusing part during the login process. When you log into your GitHub Copilot account, it redirects you to a website that asks for a device ID or something similar. I can't remember exactly what it asks for, but this ID is shown in the IDE popup. It's a bit confusing and takes a few minutes to understand this workflow. It would be helpful if they added a message saying you can find the device ID in your IDE popup. Adding more instructions to that screen could improve the login flow since it redirects you to a website to log in.

The ROI decreases if questions are too long. If it automatically accessed the content or context, questions could be asked easily with a few lines of code or statements. Currently, providing all details takes a long time, reducing productivity. Since it resides in the IDE, it should ideally have access to the codebase and know more about the context where questions are asked rather than starting from scratch.

Each user needs their license, whether it's a big team or a small team.

It's a helpful tool, but you need strong programming knowledge to use it effectively. If you don't understand Java or your objectives, GitHub Copilot might give wrong answers.

It is good at what it's designed for when handling complex coding scenarios or languages. However, there are limitations. The main issue is similar to other AI agents - requiring skilful prompting to get relevant answers. If GitHub Copilot had access to the entire codebase we're working on, it could provide more relevant and productive outcomes. But I'm not sure how security and privacy aspects would be handled in that situation.

The tool tends to give atomic answers rather than addressing very complex, multi-class-spanning questions. If it had access to more context, it might be able to provide better answers for complex scenarios involving multiple classes or large code structures.

I rate the overall solution an eight out of ten. I can recommend it, given it's affordable, because there are many free alternatives. They may need to introduce a community edition or similar option to compete and remain relevant.