What I like most about GitHub Copilot is that it helps me write code faster and reduces the time spent on repetitive tasks. While working with Angular, .NET Core, and SQL, it provides useful code suggestions, helps with generating methods, and often gives a good starting point for solving problems. It also helps me understand unfamiliar code and improves my productivity during development. Overall, GitHub Copilot makes coding more efficient and allows me to focus more on business logic rather than spending time on routine coding tasks Review collected by and hosted on G2.com.

I have not faced any major issues with GitHub Copilot so far. Sometimes the suggestions may need small modifications to fit the project requirements, but that is normal. Overall, it has been a helpful tool in my daily development work and has improved my coding efficiency Review collected by and hosted on G2.com.

What I like most about GitHub Copilot is that it makes coding faster and less stressful. It suggests code while I'm typing, which saves me from writing the same things over and over. I use it almost every day for creating functions, fixing small issues, and writing test cases. Review collected by and hosted on G2.com.

One thing I don't like is that GitHub Copilot doesn't always understand what I'm trying to do. Sometimes it suggests code that looks correct but doesn't actually solve the problem, so I still need to review everything carefully. It can also repeat the same suggestions even after I've changed the code. Review collected by and hosted on G2.com.

I use GitHub Copilot every day while developing React and TypeScript applications at work. It helps me write reusable components, generate unit tests, explain existing code, and compare different implementation approaches before making decisions. I also use Copilot Chat for planning tasks, writing documentation, and speeding up repetitive development work. Review collected by and hosted on G2.com.

Sometimes the generated code is not accurate for complex project-specific requirements, so I need to review and modify it. It can also lose context in long conversations, and responses may become slower when working on large codebases or multiple files. Review collected by and hosted on G2.com.

Honestly, the autocomplete is what got me hooked. I was skeptical at first like, how good can it really be? But within the first week I noticed I was spending way less time writing boilerplate and more time actually thinking through problems. It picks up on what I'm trying to do really fast, sometimes finishing a function before I've even thought through the logic myself. The VS Code integration feels completely natural, no friction at all. And it works across languages, which matters a lot for me since I jump between Python and TypeScript constantly. Review collected by and hosted on G2.com.

Not a whole lot honestly. Sometimes it suggests something that's totally off-base like it'll autocomplete in the wrong direction if I have a particularly unusual pattern in my codebase. You still need to pay attention and not just accept everything blindly. Also the subscription cost feels a bit steep if you're just using it solo, though for teams it's easy to justify. Oh, and occasionally it lags a bit when I'm working with very large files, but that's minor. Review collected by and hosted on G2.com.

As a QA Engineer working on automation frameworks at my company, the best part about GitHub Copilot is how it accelerates test automation by generating boilerplate code, edge‑case scenarios, and automation scripts far faster than writing everything from scratch. For example, while building Pytest suites for accessibility checks, Copilot suggested assertions, mocks, and fixtures that aligned perfectly with our framework, reducing setup friction and saving hours of manual effort.

Another major advantage is Copilot’s ability to use project context intelligently. When I’m working within our company’s test repositories, it surfaces alternative approaches — such as different ways to structure parameterized tests — so I can compare options and choose the most robust one for production pipelines. This context‑aware assistance ensures that the generated code is not only syntactically correct but also relevant to the specific QA workflows we maintain.

Overall, Copilot doesn’t replace my QA judgment; it strengthens it by cutting down repetitive effort and freeing me to focus on quality analysis, exploratory testing, and ensuring reliability across systems in a real workplace environment. Its consistency and context awareness make it a reliable partner in professional QA projects. Review collected by and hosted on G2.com.

From a QA perspective in my company’s automation projects, the biggest drawback of GitHub Copilot is that it can generate code that looks polished but isn’t always dependable, especially around edge cases or complex test scenarios. For example, while working on compliance‑driven Pytest suites logged in with my company account, Copilot often suggested repetitive or shallow test cases instead of highlighting coverage gaps. This meant I still had to spend time validating, adjusting, and refining its output before it was production‑ready.

Another challenge is that Copilot doesn’t consistently grasp the deeper intent behind QA work — such as robustness, negative testing, and regulatory compliance. Its suggestions can overlook the quality checks that matter most in enterprise pipelines. In day‑to‑day use, Copilot does speed up routine coding tasks, but it requires ongoing oversight to prevent subtle bugs and to maintain consistent standards across the entire test suite.

A concrete case: during a recent sprint, I used Copilot to generate parameterized test functions for API validation. While the code compiled correctly, it missed certain negative scenarios required for compliance. I had to manually supplement these cases to ensure full coverage, which slowed down the evaluation process.

Overall, Copilot’s cautiousness and occasional lack of depth mean that while it accelerates routine tasks, it still demands careful QA oversight to ensure reliability in professional environments Review collected by and hosted on G2.com.

GitHub Copilot helps speed up development by suggesting code snippets, methods, test cases, and logic in real time as I code. I find it especially useful for repetitive tasks, API implementations, and generating boilerplate.

What I personally like most is that it cuts down the time I spend searching for syntax or common implementation patterns, so I can stay focused on the business logic instead. Review collected by and hosted on G2.com.

The suggestions aren’t always accurate or well optimized, especially when dealing with project-specific logic or more complex enterprise use cases. At times it produces outdated approaches or incomplete code, so manual review and validation are still necessary.

In a few backend implementation scenarios, Copilot suggested methods that looked syntactically correct but didn’t fit our project architecture or align with our coding standards, which meant I still had to adjust and verify the output. Review collected by and hosted on G2.com.

As an AI automation and DevOps engineer, what I love most about GitHub Copilot is how it dramatically cuts down the cognitive load of switching between endless YAML configurations, CI/CD pipelines, and bash scripts. Instead of wasting time digging through documentation for standard boilerplate code or syntax rules, Copilot anticipates what I need and generates it instantly. This lets me stay in the zone and focus on the high-level architecture and security of the infrastructure, rather than the tedious syntax. Review collected by and hosted on G2.com.

From a DevOps and automation standpoint, what I dislike most is its tendency to confidently suggest outdated or legacy syntax for complex infrastructure-as-code and CI/CD pipelines. Because it lacks a holistic view of a multi-repository cloud architecture, it often generates code that works in isolation but violates specific security policies or network topologies. This requires me to spend extra time meticulously auditing its suggestions to prevent deployment failures or compliance risks. Review collected by and hosted on G2.com.

It provides a comprehensive interface for interacting with multiple LLMs at the same time, while abstracting away the complexity involved. It also includes several useful capabilities, such as planning, agent mode, tool calls, and more. Its performance is very good as well. It integrates well with existing vscode IDE for a unified experience. Pricing was extremely good until june. June onwards, they changed to pay per usage model and made claude models pricy. Still, its worth it. Very much a corner stone for our whole AI SDLC approach. Review collected by and hosted on G2.com.

Pricing model is very expensive based on June changes. Review collected by and hosted on G2.com.

GitHub Copilot has really improved my coding workflow. The UI easily gets integrated with Visual Studio Code feels smooth and easy to use. It gives fast AI code suggestions, help complete functions and save a lot of typing time. Performance is quick, thought sometime suggestions are inaccurate. Overall, it makes learning and coding faster and more productive. Review collected by and hosted on G2.com.

I'm currently using the student plan which was giving access to multiple AI models from different model providers (including Anthropic, Google, OpenAI...), but with the latest update to GitHub Copilot they just removed most flagship models and limit access to remaining, which is quite disappointing. Review collected by and hosted on G2.com.

What I like the most is that it saves me time on repetitive tasks. With FastAPI or Django, it suggests almost complete endpoints, models, and CRUD logic, which speeds things up quite a bit.

It also helps when I integrate APIs or write SQL queries: it's not always perfect, but it gives me a quick foundation to work from. Review collected by and hosted on G2.com.

What I don't like is that sometimes it suggests things that "seem correct," but aren't entirely so. With FastAPI, for example, it has suggested poorly constructed validations or dependencies that pass at first glance, but then fail in logic or security. Review collected by and hosted on G2.com.