Artificial Intelligence (AI) has become a cornerstone of innovation across industries, and software development is no exception. GitHub Copilot is at the forefront of this transformation within software development. By embedding AI capabilities directly into the development workflow, GitHub Copilot significantly enhances productivity, reduces repetitive tasks, and unlocks new potential for creativity and efficiency. Here’s an in-depth look at how GitHub Copilot is revolutionizing the software development lifecycle and how we use it at Liventus.
Code Auto-Completion: Writing Code at the Speed of Thought
One of GitHub Copilot’s most prominent features is its code auto-completion capabilities. As developers type in the code editor, Copilot analyzes the context—such as files open in the IDE, current file content, and comments—to suggest code snippets in real-time.
With code auto-completion directly in the IDE, comments are pivotal in guiding Copilot in generating solutions for specific tasks. Additionally, Copilot can suggest comments, enabling a collaborative loop to refine the requirements for code suggestions. This seamless interaction accelerates development, offering developers more time to focus on higher-level problem-solving and innovation by removing menial, mundane, and boilerplate tasks.
Conversational Programming with Copilot Chat
Beyond auto-completion, the Copilot chat feature provides an interactive environment for developers to engage directly with AI. During the conversation with Copilot, the Artificial Intelligence “neural network” is enhanced through developer interaction to iteratively understand the developer’s intent and suggest code accordingly. Copilot grows its knowledge of what the developer aims to achieve from accepted solutions and can learn and improve through modifications and rejections of its suggestions. AI Large Language Models (LLMs) use neural networks with millions, or even billions, of parameters that are fine-tuned during training to understand and predict text effectively.
By asking questions or issuing commands through a dedicated chat window, developers can:
- Understand Unfamiliar Code: Use commands like /explain to get detailed explanations of unfamiliar code.
- Fix Errors and Bugs: Highlight code and type /fix to receive potential solutions.
- Debug Efficiently: Ask questions like “Why doesn’t this code work?” to quickly identify and resolve bugs.
Copilot supports voice chat extensions, enabling natural language interactions through a computer’s microphone. In-line chat is another capability similar to the dedicated chat window, which allows the developer to interact with Copilot directly in line within the active code file. This evolution towards conversational programming makes AI an integral collaborator in development.
Multi-file Edits
GitHub Copilot’s utility expands beyond single-file contexts with multi-file editing capabilities. This feature allows the same type of conversational flow as Copilot Chat while applying the suggested code changes across multiple files. Developers can approve or reject proposed changes across multiple files through a “diff” view to streamline updates.
Enhanced Testing Capabilities
Testing is a crucial phase in software development, and Copilot excels at automating this often time-consuming task. Developers can use the /tests command to generate unit tests, integration tests, and Angular UI tests tailored to specific testing frameworks. This functionality not only saves time but also ensures comprehensive coverage, enhancing the application’s robustness.
Copilot Code Reviews: A New Standard in Quality
Code reviews are vital in maintaining code quality, and GitHub Copilot’s Code Review Assistant (an extension for VS Code) enhances this process. Copilot performs pull requests as the code reviewer, providing:
- Suggestions for adhering to clean code and SOLID principles.
- Identification of unused variables, hardcoded values, and security vulnerabilities.
- Recommendations for refactoring large methods and improving error handling.
Each suggestion includes a severity level, allowing developers to prioritize fixes. Copilot’s ability to propose solutions for identified issues accelerates code reviews, ensures high standards, and particularly benefits solo developers and small teams.
This is a game-changer that can greatly improve code quality, readability, maintainability, code reuse, performance, and security while saving substantial time. The developer can give Copilot custom instructions for what to focus on in the code review. The developer can also tell Copilot to propose fixes for the issues that it found when reviewing the code.
Prototyping with GitHub Spark
GitHub Spark, another AI-driven tool, takes prototyping to the next level by enabling the creation of micro apps (“sparks”) without writing or deploying code. This capability is especially valuable during the design phase, where rapid iterations of ideas can lead to more refined and user-friendly applications. For small application ides, Spark builds the entire application using AI through natural conversation interaction with the user and involves no coding.
Additional Features
Some additional features of GitHub Copilot that enhance productivity are:
- Generated Check-In Comments: Automate the creation of detailed and insightful comments during code check-ins.
- Comprehensive Documentation: The/doc command generates documentation for methods, classes, or entire files, making it easier to onboard new developers or navigate complex codebases.
- Command Line: For quick command lookups, Copilot’s integration with the command line allows developers to retrieve commands using natural language directly within the Integrated Development Environment.
- Model Choice: There are many different AI models, and GitHub Copilot allows the developer to select which model to use during their interaction with Copilot.
- Suggestions Window: The suggestions window is useful to see many different options for the solutions that Copilot suggests to resolve the problem the user is trying to solve.
Database Interactions and SQL Extensions
Copilot’s SQL Database Extensions (DB Code and SQL Server extensions) enable natural conversation-based database interactions. Developers can connect to local or Azure databases and generate SQL statements or ask database-related questions using natural language.
Studies on the Impact of GitHub Copilot on Developer Productivity
GitHub conducted extensive research and found that 96% of developers reported increased productivity, 83% of developers surveyed reported that GitHub Copilot helps them code faster, and 88% felt more productive when using GitHub Copilot.
A study published by Microsoft Research showed that developers using GitHub Copilot completed tasks 55.8% faster than those who didn’t.
Future Enhancements and the Road Ahead
Automatic .NET Updates: The Copilot Dev team for Visual Studio is projecting that Copilot will be able to perform automatic .NET updates where Copilot would handle the majority of the .NET updates for the developer and would only require a relatively low amount of manual human intervention. This would ensure developers are in the zone and focused on client deliverables instead of .NET updates.
IDE Context Enhancements: While debugging an application, Copilot will use context from the debugger session, such as the values of the data in the debug session, to find issues in the code and automatically set breakpoints for the developer to find issues proficiently.
Revolutionizing the Development Workflow
By integrating AI at every stage of the software development lifecycle, GitHub Copilot transforms how developers approach their work. From conceptualization and design to development and code review to debugging and maintenance to testing and deployment, Copilot and AI solutions enhance productivity, minimize churn, and promote focus on meaningful and more advanced tasks.
While Copilot is not a substitute for sound coding practices and DevSecOps processes, its ability to augment the developer’s capabilities makes it an invaluable tool in the modern software development toolkit. As AI continues to evolve, tools like GitHub Copilot will undoubtedly play a pivotal role in shaping the future of software development.
There is a lot of material on AI to learn and explore, including a GitHub Copilot Certification and Microsoft Learn Paths. For further suggested material, a light, high-level article describes GitHub Copilot.