GitHub CoPilot Reviews

I use the platform to assist with code suggestions and improve the efficiency of our software development teams. It helps with code assistance for novice programmers by providing them with coding best practices. 

The platform's most valuable feature is its code suggestion capability, which leverages AI to provide context-aware suggestions. It improves the coding skills of less experienced developers and speeds up the development process by reducing the time spent debugging and rewriting code.

They could improve the product in terms of integration with other tools.  Additionally, they should offer more robust support for highly regulated environments like finance or healthcare to enhance its applicability.

I have been using GitHub Copilot for a significant period. 

The solution is stable.

The product is highly scalable, as adding more licenses can be done easily without additional infrastructure costs.

The support team is knowledgeable and responsive.

Previously, I used Appian BPM, but the focus has shifted towards more modern technologies like DevOps and AI in DevOps. We switched to GitHub Copilot for its advanced AI-driven code assistance and seamless integration into the cloud-based development environment.

The initial setup was straightforward, involving license acquisition and basic configuration.

We implemented the product in-house. 

The return on investment is substantial, with an estimated ROI of 500%.

The product has a tiered pricing model that starts with a free version for individual developers but requires a separate license fee for enterprise use.

I rate GitHub CoPilot an eight out of ten. 

Most of the time, it is for code suggestions. It allows improving the efficiency and productivity of development. It is used by developers within the IDE, so the development environment.

Not that much, as I need to wait until the ongoing pilot ends to have final feedback. However, it is expected to improve coding productivity and the capacity to produce quickly and more efficiently.

The most known and important features from a development point of view are the code suggestions. It enhances efficiency and productivity in development. 

The merge request features and workflow are available. We don't use them much currently. GitHub Copilot enables the production of business projects quickly and efficiently.

I cannot really answer this one as I don't have enough experience to share this kind of feedback. Someone who uses it daily might better understand these questions.

I have used it for around six months.

Very high stability. I didn't notice any issues regarding stability.

The AI model is really scalable, so I would rate it at least nine out of ten. I am optimistic about the next releases being developed and designed.

I do not have experience contacting technical support, so I cannot provide feedback on this aspect.

Positive

Before GitHub Copilot, we evaluated GitLab with its Duo feature. Developers chose Copilot because of stronger code suggestions.

The initial setup was easy, particularly within the IDE. Minimal complexity was involved.

We use GitHub Copilot internally with the client's company. It is an internal project, and as an architect, I was involved in the discussions to decide between Copilot and other solutions.

I need to wait until the pilot we are undergoing ends to get a clear understanding of the ROI and productivity improvements.

I am not really included in the cost management part, so I don't have insights into the pricing or setup costs.

The other option considered was GitLab Duo. Based on presentations and evaluations, we chose GitHub Copilot due to its stronger code suggestion capabilities.

I would initially rate it nine out of ten based on what I know and the small experience I have had. For it to be ten out of ten, it needs more advancements in the AI part, though it is already strong. Future updates are anticipated.

I'd rate the solution nine out of ten.

I am evaluating the tool to understand how useful it is for development. I'm evaluating how smart it is.

The solution makes our work easier. If we use a particular coding style, the solution can identify our coding style. The solution increases coding efficiency. It tries to judge our coding and logging style and imitates it. It automatically suggests copy-paste functions. It saves us time.

If we try to do something that involves two to three files and functions, the tool gets confused. It gives us advice that does not work. The suggestions are not well evaluated. We need to put more effort into debugging. The tool miserably fails when multiple files are involved. The tool must improve its ability to work with multiple files.

The first suggestion provided by the product must work. Whenever the product provides a suggestion, it must validate it. It will be helpful for the users. If the first suggestion is good, we can use it. If the first suggestion is wrong, the product will not solve the problem even if we ask it multiple times.

I have been using the solution for the last one and a half months.

The tool is stable. It gives us an answer, whether accurate or not.

I rate the product’s scalability a five out of ten. It does not work well in complex coding scenarios.

The initial setup is easy. I am using the latest enterprise version.

The solution is costly. It is not reasonably priced.

I have also used ChatGPT. GitHub CoPilot is built into coding. If we are working on coding, GitHub CoPilot is a better choice. ChatGPT is a better choice if we want to understand the concepts.

Overall, I rate the solution a five 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.

GitHub CoPilot can be used for prompt engineering, delivering information to create a presentation, and comparing results from other AI tools, like ChatGPT or Gemini. You can use the CoPilot app while creating presentations, but we need to subscribe and pay an additional fee to use it. I'm truly considering talking to the management team to contact Microsoft to see if we can use that feature. It's a very useful feature.

The CoPilot app that you can use while creating presentations is very useful.

It would be useful if we could use the solution to create documents, not only prompts. It would also help if we could insert documents and create results in accordance with the documents inserted.

The tool has a feature that allows you to upload a voice document or a recording, and it can export the transcription for that recording. This transcription is not available in all languages. I would like to know whether the solution plans to expand the number of languages because I want to know if there's also a plan for Hebrew transcription.

There are limitations because you have to subscribe and pay more money to use the GitHub CoPilot tool.

I have been using GitHub CoPilot for a few months.

I rate the solution an eight out of ten for stability.

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

I've tried to contact Microsoft, but I haven't received a response. I'm not impressed with the technical support.

The solution's initial setup is pretty straightforward, but I'm not as impressed as ChatGPT and Gemini.

With GitHub CoPilot, we have more opportunities and can approach more people in less time. The solution shortens the time it takes to finish a certain task.

We have a license but need another one for the GitHub CoPilot tool.

Overall, I rate GitHub CoPilot 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.

Whenever I need to write test cases for my development code, I just give the command to Copilot, and it automatically helps me write them. Then I just need to review them. It also really helps me write the skeleton of the basic design. It won't give me a 100% solution, but it gives me a 75% solution. Then I just need to review it, make some cosmetic changes, and it's ready. 

Overall, where I used to spend a whole day on test-related activities, now I just need to spend two to three hours, and I can complete my job.

It's helped reduce engineering effort. If I can complete my job in half a day, then all the engineers can complete their jobs in half a day. Ultimately, it saves cost and optimizes performance. 

From the company perspective, if we needed to spend a million dollars, we can now reduce it to at least a hundred thousand dollars.

Copilot's code review, test case generation, and chat features are most valuable to me. When I write code, I feel like I have someone sitting with me to help me. Wherever I'm stuck, I just ask a question, and it provides guidance that I can use.

In a few cases, the results aren't correct, so that needs improvement. Also, it would be great if the results could be presented in different formats, not just text. As engineers, it takes time to read through text-based results. 

If they could provide diagrams or basic design architectures, it would be easier to understand and take less time to review. So, as of now, image functionality is not provided by GitHub Copilot, and it would be great if they could add that.

So, I would like to see improvements in the result generation, more results, different result formats, and image functionality.

I have been using this solution for four months. I work with the latest version.

I would rate the stability a nine out of ten. It is a stable product. Copilot is a good thing when you're writing code. It assists you wherever you need it. 

On the other hand, there are a lot of benefits to this feature, like suggesting code before you write it and suggesting the optimal way to write it, which is really helpful. 

Apart from that, it helps with writing test cases and suggestions for heavy algorithms where you need to put in a lot of brainpower. So, it makes things easier.

On the other hand, the negative points, Copilot still hasn't reached a point where we can trust it 100%. Sometimes, when we raise a query, it stops working intermittently. So, at that moment, we need to look for other options. It's not, as of now, 100% reliable. Sometimes, it stops working; sometimes, the output has issues. So, we need to be careful with information.

I would rate the scalability an eight out of ten. Sometimes, we face high traffic on Copilot, and it stops responding to our queries. It's happened a few times, maybe three or four times out of a hundred.

It's not 100% reliable. Sometimes, we stop receiving responses. We have around 500 end users in our company. It is used on a daily basis. 

The customer service and support are okay. 

Positive

We switched to GitHub because it's more appropriate and saves me time. On Google, I need to spend time searching for something, typing it in, and then getting a bunch of solutions out of which I need to pick the best one. 

Copilot, on the other hand, gives me the best answer out of all the possibilities with just one query. That's a big benefit.

I would rate my experience with the initial setup of this product a seven out of ten, where one is difficult, and ten is easy to set up. 

It is not complex. I gave it a seven because there were a few minor technical challenges with the initial login. It wasn't as smooth as logging in to Gmail or Microsoft Outlook, but I was able to log in after a few tries. 

So, it is a bit tricky. These are just minor challenges, not completely smooth. 

We use the licensed version, not a cloud version. We purchased a license and installed it.

We did everything ourselves. I was able to do it alone; it didn't require much effort. So, one person can manage both the deployment and maintenance.

It took only one day to deploy it. It's not really a deployment in the traditional sense. We requested a license key from Microsoft, and then we used that key to log in from our laptops. We configure it with our development environment, and it works smoothly.

If you invest some amount, it will definitely reduce your production cost. I would rate the ROI an eight out of ten, where one is low ROI and ten is high ROI. 

I would rate the pricing a five out of ten, where one is very cheap and ten is very expensive. So it's affordable.

There are no additional costs to the standard license. So the price is fixed.

This is the first time I've used a tool like this that assists with code writing. Before that, I used a simple Google search. 

Moreover, Google has recently launched its feature, but it's not as mature as Copilot's. They're in the very early phase.

Developers, testers, front-end, back-end, any developer can use it. It's really beneficial for them. On the other hand, testers can also explore test cases with Copilot. It'll help them, too.

Overall, I would rate the solution an eight out of ten. 

I use the solution in my company since, as a data engineer, I also work with Python. For writing a code or for reviewing the code, I'm using code on it.

The most valuable features of the solution stem from the fact that it gives you suggestions when you start typing.

I have not used the product much to be able to comment on what needs improvement in it. With the passage of time, I believe that there will be improvements in its prompt response, like ChatGPT, considering that GitHub will get more data. With time, the product will mature.

The product's price is an area of concern where improvements are required. ChatGPT is free, and people would rather prefer it over GitHub CoPilot.

Considering the way GitHub CoPilot works, it can improve its connection with technologies like SQL. Right now, I can't write an AWS Glue job in GitHub CoPilot. In the future, if the tool can improve and include multiple technologies and languages, I see that they have a scope to offer better scalability.

I have been using GitHub CoPilot for less than a month. I am just a consumer of the tool.

Sometimes, when the product doesn't function properly because of some network issue, I prefer to use ChatGPT. In ChatGPT, you can ask any general question.

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

Before GitHub CoPilot, I worked on ChatGPT, so I had to give the prompt to get what I wanted. ChatGPT is also a very helpful tool.

I currently work with GitHub CoPilot and ChatGPT.

ChatGPT gives the wrong output at times, after which you need to correct it. GitHub CoPilot gives correct codes since it always works on the existing codes in GitHub. GitHub CoPilot gives a correct output.

The product's initial setup phase is easy, but sometimes, if your company has firewall issues, it gives errors.

There is a need to pay around 10 USD to be able to use the solution.

I am planning to use this AWS Bedrock and another tool from AWS for its AI-based features. I have not decided on the tool I plan to use. I want to learn and see what AWS is doing with its products.

GitHub CoPilot improved my code efficiency by helping me save time. The tool gives me ready-made codes, which I review and then implement.

Speaking about GitHub CoPilot's ability in terms of context and syntax suggestions, the tool is helpful.

The product is easy to integrate.

I recommend the product to developers who plan to use it. It is not a free tool forever, meaning it is only free for a month.

I rate the tool a six to seven out of ten.