If you’re looking for a GitHub Copilot review, you’re already well aware of the transformative impact of artificial intelligence (AI) for developers. Code assistants claim to make coding faster and reduce errors. But you may wonder how developers are really using code assistants and whether they generate quality code. As AI enthusiasts, we’ve done the research and are here to help you answer those questions.
While developer tools built on generative AI may feel very new, using artificial intelligence to help with software development is probably older than you realize. One of the earliest attempts to automate programming can be attributed to Grace Hopper, a pioneering computer scientist known for her work on the UNIVAC I and COBOL programming languages. Hopper’s creation of the first compiler in the late 1950s revolutionized software development by translating high-level programming languages into machine code, which automated the process of code translation.
AI has come a long way from symbolic AI in the 1970s. With recent breakthroughs in deep learning, today’s AI copilots represent a significant leap toward intelligent code generation. Modern AI assistants like GitHub Copilot help developers generate code, debug it, and maintain it.
Read on for our developers’ expert opinion on what this AI is good for, its limitations, and whether it’s worth it.
Source: Wikipedia
What is GitHub Copilot?
GitHub Copilot is a leader among a new class of AI-assisted copilots for developers.
A copilot, also known as a pair programmer, is an AI-powered virtual assistant that helps boost developer productivity. Copilots use large language models (LLMs) for natural language processing (NLP), which means they can have human-like conversations with users.
GitHub’s popular cloud-based software development platform lets developers create, store, manage, and share code all in one platform. GitHub includes access control, feature requests, continuous integration, task management, and more—and now an AI-driven code assistant.
GitHub Copilot is powered by OpenAI, the large language model (LLM) behind ChatGPT. It uses advanced machine learning to provide real-time code suggestions. We’ll walk you through GitHub Copilot’s effectiveness, best practices, and what the future of AI in software development might hold.
Investigating GitHub Copilot’s effectiveness
In the first half of 2024, the Intellias developer team evaluated several AI tools. The information below includes our insights and practical findings. Bear in mind that the technology is moving quickly, and this review represents a snapshot in time.
Cost efficiency in software development
AI tools make software development more efficient. Any good Copilot speeds up development and optimizes resource use by automating repetitive coding tasks and reducing manual work. A Copilot also frees up time for developers to focus on more complex and creative tasks, helping their companies innovate and compete more effectively.
We found that GitHub Copilot offered more efficient solutions than Tabnine and Amazon CodeWhisperer. GitHub Copilot was much more accurate in generating useful code snippets, which reduced manual correction time.
That said, our team subsequently built our own software copilot for internal use. Since rolling out IntelliAssistant-our proprietary AI-powered assistant to our clients, it’s proven to be 3.8x more cost-effective than GitHub Copilot. This cost-saving potential is especially beneficial for startups and small businesses.
Future development of programming languages
As AI-generated code becomes more common, it might change programming languages themselves. New coding methods and tools could emerge to support AI’s capabilities better, maximizing the benefits of these AI tools.
Our GitHub Copilot review demonstrated superior support for emerging programming languages. GitHub Copilot currently supports more than ten core languages, and most of its features work with any language. While the technological landscape is changing faster than ever, you can be confident that GitHub Copilot will continue to cover mainstream languages and frameworks. It’s a solidly future-proof choice.
Impact on software development methodologies
Copilots could revolutionize traditional software development methods. For example, AI-driven insights and automation can enhance Agile and Scrum practices. Development approaches will increasingly integrate AI tools. Developers may be expected to use AI to help with coding, analysis, and optimization throughout the development lifecycle.
Since the best answer to an efficient AI-augmented development cycle is tight integration between code generation and analysis tools, GitHub Copilot is well-positioned for the evolving nature of software development.
Impact on the workforce and job market
Any technology that automates routine work comes with the potential to displace jobs. However, programmers who develop new skill sets to work effectively with AI will ensure their continued relevance. These skills include mastering AI and machine learning technologies, understanding data science, and adapting to new development paradigms where humans and AI work together.
This process is already well underway. As of mid-2023, 92% of developers told GitHub they were using AI in software development.
But IT leaders should also foster innovation and continuous learning within their teams. By providing resources and support, leaders can help developers acquire new skills and stay current with developing technology. Among other features, our AI copilot, IntelliAssistant, gives personalized learning recommendations based on employee skill gaps and career aspirations.
Enhancing code quality with GitHub Copilot
If you’re thinking of adopting GitHub Copilot, you’d be right to wonder about its impact on code quality and maintainability. Can a pair programmer significantly improve coding practices and make managing your codebase easier? Software projects are getting more complicated, so maintaining high code quality is foundational to long-term success and scalability.
In the first half of 2024, our team explored how GitHub Copilot and competitors influenced code quality. These are our insights and recommendations for various coding environments.
Influence on code quality and maintainability
GitHub Copilot helps developers write cleaner, more efficient code by recommending improvements and refactoring opportunities. This support can keep your codebase maintainable, reduce technical debt, and help keep projects scalable and easy to update.
Impact of codebase size and complexity
If you’re going to use a copilot, it’s important to understand how the tool performs in different types of projects. That’s because the effectiveness of a copilot can vary depending on the size and complexity of your codebase.
Even GitHub Copilot may struggle to provide accurate suggestions in larger, more intricate projects when numerous dependencies and interactions complicate the code. But for smaller and less complex projects, GitHub Copilot’s recommendations can be highly effective, significantly speeding up development and improving code quality.
Knowing how a coding assistant performs will help teams use their strengths and be prepared to handle potential challenges in more complex scenarios.
Performance variation based on programming language and technology stack
As mentioned above, GitHub Copilot supports many popular programming languages, including Python, JavaScript, TypeScript, Ruby, and Go.
GitHub Copilot’s performance is generally more accurate and helpful for well-supported languages, providing developers with relevant and precise code suggestions. However, the tool might be less effective for niche or less commonly used languages, where its training data is limited.
Teams should consider language support to make informed decisions about integrating Copilot into their development workflows across different languages and technology stacks.
Importance of clear naming conventions
Clear and logical naming conventions are crucial for readability and maintainability because they make it easier for team members to understand and work with the code. We found that GitHub Copilot could understand our project’s rules and best practices to suggest solutions in line with our naming conventions and standards. This leads to better-quality code, which means smoother collaboration and easier onboarding for new developers.
Balancing benefits and limitations of GitHub Copilot
In our team’s analysis in early 2024, GitHub Copilot Business was the clear winner, beating Tabnine and Amazon CodeWhisperer on every metric we tested, including overall satisfaction with the tool. You can find the breakdown of our scores on page 12 of the AI Engineering Productivity Cookbook.
However, while GitHub Copilot offers many benefits, team leaders and developers must also understand its limitations. Knowing where Copilot excels and where it falls short is key to making the most of this tool and integrating it into your software development process. To use Copilot effectively and avoid potential pitfalls, keep these points in mind:
Current limitations of AI Copilots
GitHub Copilot is not perfect; no software is. Its suggestions can sometimes be less than optimal or downright incorrect. This is especially true for complex logic or edge cases. Developers need to review and validate the code generated by Copilot to make sure it meets their project’s requirements and standards.
Edge case handling: Copilot might struggle with edge cases that aren’t well-represented in its training data. This can result in code that fails under unusual or extreme conditions, requiring developers to pay extra attention to these scenarios.
Context sensitivity: Copilot’s effectiveness depends on the context of the code. It might not always understand the broader context of the project, leading to suggestions that fit locally but don’t align with the overall design or requirements of the project.
Tool-specific knowledge: Another limitation is that Copilot might not fully understand the specifics of certain tools or frameworks. It might not return the best suggestions for specialized tasks or less common frameworks, leading to inefficient or incorrect code. In these cases, the tool’s recommendations can be more of a hindrance than a help.
Security concerns: Copilot also has limitations when it comes to security. The code it suggests might not always follow the best security practices, potentially introducing vulnerabilities. Developers must be vigilant in reviewing any AI-generated code to ensure it adheres to security standards.
Debugging and problem-solving: Lastly, while Copilot can assist with many coding tasks, it doesn’t replace the need for solid debugging and problem-solving skills. Developers still need to troubleshoot and debug their code, as Copilot might not always pinpoint the source of an issue or provide the best solution.
Role of human expertise and judgment
Remember, it’s Copilot, not Pilot. It’s not meant to be in the driver’s seat. This tool is an assistant for developers, not a developer itself. Despite its impressive capabilities, a Copilot cannot replace a human developer’s critical thinking and problem-solving skills.
As with any tool, careful oversight will ensure it helps rather than hinders your efforts. Developers familiar with the tool’s limitations are in the best position to use it as a helpful assistant while using their expertise to ensure code quality and security.
You’ll need human expertise to evaluate Copilot’s suggestions, make necessary adjustments, and ensure the final code is robust and efficient. Put processes in place to review and validate any code Copilot generates so the final product meets the project’s requirements and the company’s standards.
Overall effectiveness and future potential
GitHub Copilot is a powerful tool that significantly enhances developer productivity and code quality. Its ability to provide context-aware suggestions and automate repetitive tasks makes it a valuable addition to the development workflow. As AI technology continues to advance, tools like Copilot will likely become even more sophisticated, further transforming the software development landscape.
By embracing AI tools and understanding their limitations, developers can improve their workflow, enhance code quality, and stay competitive in the fast-evolving tech industry. That said, these tools do come at a cost.
After working with GitHub Copilot and experiencing its benefits, Intellias developed our own AI-powered code assistant. Our AI tool provides similar capabilities and features tailored to our specific needs. Since most companies aren’t in a position to develop a Copilot in-house, we’ve now rolled out IntelliAssistant or our clients—at a very cost-effective rate.
AI use cases in software development
You’ve seen how AI tools can potentially save time and improve code quality, but what about real-world applications? How are companies actually using AI copilots to transform their workflows? Let’s dive into some concrete use cases to see how AI makes a difference.
Enhanced code generation
AI-powered tools can generate boilerplate code, which cuts down the time developers need to spend on repetitive tasks. By providing relevant code snippets based on the context of the project, Copilot helps developers focus on more complex and creative aspects of coding.
For example, we used GitHub Copilot to streamline our workflows and reduce errors. This not only sped up development but also ensured consistency across the codebase. We later developed our own IntelliCopilot to offer similar capabilities at a more cost-effective rate.
AI-powered testing
Since testing is a critical aspect of software development, it’s great to see how AI tools are revolutionizing this process. They can assist in generating test cases, identifying edge cases, and suggesting improvements to existing tests. By automating parts of quality engineering and testing process, AI helps developers maintain high-quality code with less manual effort.
When implementing a new feature for a client, our team used GitHub Copilot to generate initial test cases. Although developers still needed to review and refine these test cases, the automation provided a solid starting point. AI sped up the testing phase and helped ensure comprehensive coverage. This proactive approach to testing made sure our software was robust and reliable.
Natural language to code conversion
One of the most impressive capabilities of AI tools is the ability to convert natural language descriptions into code. Users can describe the functionality they need in plain English, and Copilot will generate the corresponding code.
During a cross-functional project, non-technical team members described their requirements in plain language. We used GitHub Copilot to translate these descriptions into initial code snippets, which developers then refined and integrated. Copilot’s natural language capability was more than a time-saver—it improved collaboration and understanding between technical and non-technical team members, making the development process more open and inclusive.
AI-Driven project management
Beyond coding, AI tools also enhance project management. AI can analyze project data, track progress, and provide resource allocation and timelines insights. For instance, tools like GitHub Copilot can suggest improvements to project workflows, identify bottlenecks, and recommend optimal strategies for task completion.
AI-driven project management tools give teams a way to streamline processes, meet deadlines more consistently, and adapt to changing project requirements more effectively. Our experience with IntelliAssist yielded significant productivity improvements, with 65% of participating engineers reporting over a 25% increase in productivity.
Seamless integration into development environments
GitHub Copilot and similar AI tools integrate seamlessly into popular Integrated Development Environments (IDEs). This integration ensures that developers can access AI-powered features without disrupting their workflow. A Copilot can provide code suggestions directly within the IDE, allowing developers to accept, modify, or reject these suggestions in real-time.
Our IntelliCopilot integrates seamlessly into popular IDEs, including Visual Studio, Visual Studio Code, and IntelliJ IDEA. This smooth integration enhances the end-to-end development experience. IntelliCopilot makes it easy for developers to adopt AI tools and incorporate them into their daily routines without disruption. We’ve seen this seamless integration lead to more efficient development processes and a smoother workflow, including:
- 53% more focus on task completion
- 81% reduction in frustration related to routine tasks
- 79% acceleration in information retrieval
- 51% enhancement in code review efficiency
Which copilot is right for your team?
The question isn’t whether to adopt AI tools for software development; it’s which copilot is right for your team.
After evaluating several AI copilots, our GitHub Copilot review reveals that it stands out as a decent choice for developers. Its robust features, including dynamic code suggestions and customization options, make it a leading tool for enhancing coding efficiency and productivity. However, if you need a more cost-effective solution, contact us and learn how AI Copilot Program by Intellias can help you make your software faster, cheaper, less buggy, and more inclusive.
Does the future of software engineering include an AI copilot? Learn how we can help you make your software faster, cheaper, less buggy, and more inclusive. To find out more, contact our experts today.
