GitHub CoPilot Reviews

My usual use cases for GitHub CoPilot include creating simple system architectures and getting input on using AI for building solution architectures. I also use it for a lot of test documentation and automation, and we have a lot of technical documentation that needs to be done based on the code. A little bit of code debugging is being done, but not extensively.

To write some simple codes, simple feature functionality code, and to debug that code is what we are using it for.

Efficiencies with GitHub CoPilot have improved by 30%, which means a quicker go-to market and a simplified way of documenting technical designs. The code itself has become better and more structured; we no longer wait for people to find the time to do proper documentation. It becomes a part of the process because it doesn't take too long to get that done.

I use GitHub Copilot for various tasks, and its usage varies depending on the case and sometimes on the task itself. 

Occasionally, when I'm tasked with rebasing our entire codebase, I use Copilot to summarize the purpose of the file to gain an understanding of what the entire code is meant for. Afterward, I provide suggestions on how to minimize the codes and logic so that the code is refactored and redundant code is eliminated. This saves me a lot of time. Before using Copilot six months ago, the process was cumbersome as we used Chargebee, which required copying the code and pasting it into Chargebee. 

However, GitHub Copilot's extension is integrated into VS Code, making the process more efficient. By simply hitting a keyboard combination, I can communicate directly with the agent within the Code Editor. This integration makes the process seamless and easy to use, as it has the code context baked into the Code Editor.

Previously, when using other AI agents, I had to provide the code without the context of its usage or ultimate goals. These agents only checked the code for logic without understanding the bigger picture of the project. However, Copilot is integrated into my environment, providing the context and the bigger picture of how the code is used throughout the project. This makes the suggestions more helpful since the agent works better with more context. Copilot's integration saves a lot of time, which previously required cumbersome processes involving Chargebee. 

With Copilot, a keyboard combination brings up a chat interface, allowing direct communication with the agent within the Code Editor. This seamless integration has made it very easy to use.

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.

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.

I am maintaining my code as a code repository and using my student account with GitHub CoPilot.

GitHub CoPilot is easy to use even for beginners and it supports the integration with CI/CD tools. It is helping organizations by creating frameworks. CoPilot is a main selling point.

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. 

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.

I primarily use GitHub Copilot for coding and also utilize natural language prompts to get some code suggestions. 

Additionally, I use it after the development team completes their work to check for any vulnerabilities, as it provides a vulnerability report.

In terms of functionality, the code suggestions in GitHub Copilot are particularly helpful for project development. It provides valuable insights into errors during compilation, allowing me to ask within Copilot itself for solutions without needing to search the internet or use platforms like Stack Overflow. This capability makes problem-solving more efficient.