GitHub CoPilot Reviews

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.

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.

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.

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.