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Artificial intelligence (AI) tools have revolutionized how we approach technical challenges. Whether improving customer experience, optimizing business operations, or enhancing product design, AI has become a valuable tool for solving complex problems in the tech industry. Copilot is one of these tools, and this article will cover Copilot use cases from our practice that helped us to write more efficient and faster code.
What is Copilot?
Copilot is an AI-powered code editor that uses Machine Learning to suggest code snippets based on the context of what the developer is working on. By analyzing the code in real-time, Copilot provides suggestions for autocompleting lines of code or even entire functions, significantly reducing the time developers need to spend searching for solutions.
One of the critical benefits of Copilot is that it can help developers work more efficiently, enabling them to focus on the big picture of their project rather than getting bogged down in individual lines of code. This benefit can be especially helpful when dealing with complex technical tasks.
When a developer types what they need from a function, GitHub Copilot will generate the code to execute that function with comments explaining how it works. This function saves the time and effort of developers while they work on routine tasks. GitHub Copilot also learns from the codebase. As developers use it, the program gets more familiar with their coding style and develops more relevant and accurate suggestions. GitHub Copilot requires your supervision and constant review, as it may not always generate accurate code.
Copilot was introduced by GitHub and OpenAI to power code completion. ChatGPT is a separate AI tool that works as a template for a specific code task or summarizing complex code. ChatGPT is also a product of OpenAI. These tools are paired to achieve better results and generate high-quality code. Copilot can suggest code snippets for specific tasks, while ChatGPT can provide more detailed explanations of tasks or technologies
We actively integrate Copilot and ChatGPT into our development practices and internal processes. Further, we’ll describe the use cases of those tools in our practice and tell you about the results of their application.
Use Case I: Enhancing code quality with Copilot, ChatGPT
Summary: Introducing Copilot, ChatGPT allowed us to save time for writing a boilerplate code and improve overall code quality. Focusing on the four primary directions brought us to these results.
1. Reducing errors
One of the main benefits of using Copilot is that its correct application helps to reduce errors in your code. By suggesting contextually relevant code snippets, Copilot ensures your code is syntactically correct and free from common mistakes. This can be especially helpful when working with complex or unfamiliar codebases, where making mistakes is easy.
2. Improving consistency
Consistency is vital when it comes to code quality. Code that is consistent and follows established patterns is easier to read and maintain, saving time and reducing the risk of bugs. Copilot improved the consistency of our code by suggesting code snippets that follow established patterns and conventions, such as naming conventions, formatting, and coding style.
3. Encouraging best practices
Copilot also encourages using best practices in your code. For example, it can suggest code snippets that use secure coding practices, such as input validation and sanitization. It can also suggest code snippets that follow performance best practices, such as using caching or optimizing database queries.
4. Saving time
By suggesting code snippets, Copilot helped us to automate repetitive coding tasks and reduce the time required to write and debug code. This benefit allowed us to free up developers’ time, cut off the volume of tasks related to creating repetitive code lines, and let them focus on more critical tasks, such as architecture and design. Copilot saved the time we took to write repetitive code and let us search for code syntax faster.
Use Case II: Improved estimating accuracy
Summary: We speeded up a time to market by 30% by providing more competitive pricing to our clients while maintaining profitability.
Project estimates are essential to project management. They help stakeholders understand the scope of a project, its expected timeline, and the resources needed to complete it. But doing accurate project estimates is a complex and time-consuming task.
When approaching a complex web development project, we found our estimates consistently over budget. A solution was needed to develop this web application more efficiently and accurately. One of the significant challenges was the time required to build analytical tools and data processors to analyze data. Regular methods implied tons of manual work, were prone to errors, and counterproductive. We decided that utilizing Artificial Intelligence would allow us to analyze data faster and make more informed decisions. Thus, our team turned to the power of GitHub Copilot + GPT to help us speed up our time to market.
In the context of project estimates, Copilot assisted us with understanding the exact volume of resources and time span needed to complete the web development project. In particular, Copilot suggested code snippets for setting up a login page, creating a user database, and handling user authentication.
ChatGPT is another AI-powered tool we used to develop more detailed project estimates. Since ChatGPT is a language model that can generate text based on user input, it can answer questions, explain, and generate entire paragraphs or articles. Therefore, ChatGPT was leveraged by us to elaborate on more detailed explanations of specific tasks. Understanding the scope of tasks with the help of ChatGPT suggestions helped us to shape a more accurate roadmap and present it to stakeholders.
In conclusion, using GitHub Copilot + GPT became the solution we looked for to build a cost-efficient and competitive project estimation plan. We could provide more competitive pricing to our client and move on with implementing the proposed roadmap. At the same time, this plan mitigated the risk of budget overruns.
Use case III: Automating tests with Copilot
Summary: Unit testing with the help of Copilot significantly saved developers’ time and reduced the number of errors made during tests.
As a software development company, we know that writing tests can be time-consuming, and it’s easy to make mistakes that lead to incomplete or ineffective test suites. GitHub Copilot analyzes the code and suggests test cases based on the code’s behavior. For example, when we created a function that calculates the square of a number, Copilot generated test cases to verify that the function returns the correct value for different inputs.
Receiving Copilot’s suggestions on using different testing frameworks and libraries based on the written code was helpful. When our developer was writing tests for a Python application, Copilot prompted using the PyTest library for testing.
To start a test automation project, we installed the tool in our development environment. Copilot works with Visual Studio Code, so we installed that program next. We created a new test case in Visual Studio Code. Following that, we asked Copilot to generate the corresponding code. Copilot analyzed the test case and generated the code.
This code defines a new test suite called “Adding two numbers” and creates a new test case that checks whether the function addNumbers() correctly adds two numbers together. The test case sets up two variables, x, and y, with values of 5 and 10, respectively, and then calls the addNumbers() function with those values. It then checks whether the result is equal to 15, which is the expected sum of 5 and 10.
Once the code was generated, we could run tests like any other tests and monitor their progress. In general, our test automation project wasn’t processed by Copilot alone. It’s fair to conclude that the program rather helped us to move forward with the project much quicker.
In addition to generating code for test cases, Copilot can also help you write other parts of your test suite, such as setup and teardown code or more complex test cases.
Conclusion
As we can see, Copilot and ChatGPT offer undeniable advantages as assisting tools:
- Copilot greatly assists with writing boilerplate code. This code can be reused in multiple cases, which saves overall development time.
- Copilot gives your project a good start as its suggestion mechanism shows directions.
- The tool is built in a way that it doesn’t overwhelm you with auto-suggestions every step of the way. We can safely say it is easy to use.
At the same time, developers must carefully review Copilot’s suggestions and make sure they make sense in the context of the application being tested.
