GitHub CoPilot Reviews

We use GitHub Copilot to enhance our productivity while coding. While GitHub itself serves as our code repository, Copilot provides suggestions and reviews as we work on our code. Copilot's effectiveness depends on the quality of its training data and how we integrate it into our workflow. It offers valuable feedback and performs various types of analysis, including statistical and dynamic, to help ensure code quality before it goes into production.

The effectiveness of GitHub Copilot depends on the number of transactions and your subscription plan. Copilot performs well with generic use cases, but the training dataset also influences its performance. For instance, companies with client-server architectures can benefit from Copilot as it learns from exposure to more repositories and user feedback. The more you interact with Copilot and provide feedback, the better its suggestions become.

GitHub Copilot can improve productivity by up to 40%, especially for experienced developers. Senior developers often spend more time reviewing code, which can make the code review process a bit longer.

It comes out with more unique codes; the training dataset needs to be larger.

Logical coding is better done by a human being. It can be improved.

The limitation is based on the training dataset and the number of repositories. The migration can be a little easier for people to do it. Also, the lack of awareness and understanding of what Git is and what GitHub is can be handled quickly. There could be more awareness.

I have been using GitHub CoPilot for over a year.

I have not faced any stability concerns because it's not a large base. If there are concurrency issues with multiple people doing the same thing simultaneously, it is for a mid-sized or a large bank.

You can scale GitHub. I've used it for 300, but other companies are looking at thousands. It has a lot of potential to expand its user base, as indicated by its roadmap.

The initial setup is peaceful. I have not personally installed it. I've installed only the GitHub. The Copilot is more at the server level.

It doesn't take much time. The configuration is pretty much relatively simple.

It saves 40 percent of the cost. For productivity, it's close to about 70 percent.

Copilot costs around $20 per month. While it's currently priced at $20, startups or organizations may be able to get a better rate, particularly for enterprise licenses, which might be available at a lower cost, such as $19 or $18. At the moment, there isn’t a direct competitor offering a similar product at a lower price.

If you're considering a complete CI/CD pipeline, you'll need to integrate various tools such as Jenkins for builds and ensure connectivity with platforms like AWS or Azure. All these aspects must be managed effectively. Therefore, it's essential to have a dedicated configuration management team to oversee and maintain the pipeline.

We're working on developing our in-house AI tool. GitHub Copilot, closely integrated with Microsoft products like .NET, offers extensive support for various programming languages. For example, it supports a wide range of languages, while a customized tool might only cover around seventy percent of the same range.

It definitely can do a little bit of statistical analysis and give recommendations.

I would recommend considering GitHub Copilot. If you already use another tool, be aware of the migration effort required to switch to Copilot. Assess the process of adopting and onboarding Copilot to ensure a smooth transition. If you're moving from one tool to Copilot, you'll need to evaluate the migration effort involved. It’s essential to have early adopters to facilitate this process.

Overall, I rate the solution a nine-point five 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 can get suggestions by giving some prompts for code generation and code validation. And even when we need to understand already written code, we'll use it to explain.

Sometimes, we are able to get benefits from understanding the existing code, writing new code, and even validating our already written code.

So, Copilot enhanced our productivity through its AI capabilities.

Code generation is the most helpful feature. It's easy to use.

Sometimes, if it is a bigger piece of code, it breaks in between. So the performance could be improved.

Sometimes it requires some manual validation, whether the given result is correct or not. Maybe it should be more accurate.

I have been using it for six months. 

It's good. I didn't try increasing the number of users or anything as such, but it's working well.

In my organization, almost 200 to 300 people use it.

The initial setup is straightforward. No issues. It took a couple of minutes, not more than that.

We haven't deployed it in any special way. We just installed it, and we are using it.

The number of people required for setup depends on the project size.

We saw at least 20% ROI, we are able to get the benefit of using Copilot in our day-to-day life.

Overall, I would rate it a nine out of ten. But it still has a long way to go.

I would recommend it to others.

If I want to find things quickly and easily, I'll use it for content, but I don't cut and paste. It's like I'm checking my ideas.

If you had a Microsoft Teams environment, we could use it to create a virtual agent or something. I know the general things I can do with AI and chat engines.

I use it almost like a search engine, but it goes a step beyond. A search engine shows you many results, and you have to read everything to put it together. But with CoPilot, you ask a question, and it gives you a summary of all the websites out there. So it's faster learning.

It handle complex tasks as well. It is able to generate results that I want. 

The problem arises when a bot is not well-designed, which frustrates customers.

For example, with our local Comcast cable company, if you need to repair something, you go on the app. They ask, "What's wrong?" You say, "My power cord melted. I need a new power cord." But the bot responds, "Oh, your Internet's not working?" 

So, it doesn't recognize unusual issues. Then, they make you go through troubleshooting, saying, "Your Internet looks good, but your device isn't plugged in." It's like, "Yes, I know. I want to speak to an agent." 

And it takes another ten minutes of trying different things before you finally get to an agent.

This is an example of a poorly designed bot.

I have been using this solution for about a year.

It's the self-service type. We just use it very simply.

We just use it for the same reasons I do. We do work with other providers and program virtual agents or bots. Those are all the rage now. You put them on your website. You used to have simple ones like "I can check your deposits or cancel a check for you," but they're limited to what you program. 

But if you expand a bot like Swifter Robot with artificial intelligence and natural language processing, then it can almost be like a person. Then, you can do some really cool things with it. 

I haven't encountered any issues. When I do, I just call our IT group. I haven't had any problems with it, unlike OneDrive.

I believe it's just standard with Microsoft, so you download it with their installer.

Increased productivity and improved customer experience can count as an ROI. If customers need to know something, they'll either wait five minutes for an agent or they can just go ahead and find the answer themselves. Most will choose the latter. The problem is poorly designed bots that frustrate customers. 

We have a team that decides on the best tools, like Google Digital or other products.

I would recommend it because it's part of the Microsoft package, it's easy to use, and I haven't found any of the answers to be outrageous.

Overall, I would rate it a seven out of ten because I haven't used it enough.

I just installed the product idea in my ID extensions, as it will be handy for auto-completions and suggestions. Sometimes, I use the tool to simplify the code and for documenting the code.

I like how GitHub CoPilot analyzes individual docstrings and keeps the method context relevant. Instead of throwing random codes, GitHub CoPilot sticks to a class of more context-relevant code generation, which I like.

Speaking about the code suggestion capabilities of the product, I would say that though it is good, there is a need to develop it further. In our company, when we have more dependent classes opened, we realize that GitHub CoPilot needs to be improved. GitHub CoPilot needs to analyze the whole package and try to give more relevant codes.

If the product plans to provide more extensions, then adding Eclipse would be helpful. There are no extensions provided by the product for Eclipse.

I wish the context window gets expanded, as it would be good for the product, especially since I am currently using some custom packages and custom classes. GitHub CoPilot needs to give some generic code generation instead of analyzing my classes and giving relevant codes. Apart from the aforementioned area, there are no major challenges in the tool.

I have been using GitHub CoPilot for ten months. I am a customer of the tool.

There are no stability issues with the product.

I don't know how to comment about the scalability feature of the product since it is just used as an extension.

I have not worked with other solutions, and I believe that GitHub CoPilot has a monopoly in the market because there is no other free tier version of other products in the market variable that meets my expectations.

The product's initial setup phase is easy.

The solution can be deployed in five minutes if you have activated a GitHub CoPilot account.

I have definitely experienced an ROI from using the product. It saved my company 30 percent on costs and time.

The pricing is reasonable. I think it is 10 USD per user.

The AI integration in the product is good.

If you are new to coding and you want to save some time by not writing a course from scratch, then you can definitely use GitHub CoPilot, as it saves a lot of time in the initial stages of development but not in the more advanced production-ready stages.

I rate the tool an eight 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 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. 

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.

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.

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.