In this Technode Global Q&A with Shuyin Zhao, Sr. Director of Product Management at GitHub, we learn about the latest advancements in GitHub Copilot X and its integration with GPT-4. As Copilot X becomes a more integrated AI assistant throughout the development lifecycle, developers can expect to see improvements in code generation, logical reasoning, and better summations of documentation, according to Zhao. She also delved into the challenges faced in ensuring seamless integration with various developer tools and platforms and the impact of AI-generated tags for pull requests.

In addition, she shared insights on addressing concerns around AI-generated code, the potential role of voice commands in software development through Copilot Voice, and the implications of Copilot X on education and skill development for both new and experienced developers.

Read on.

Shuyin Zhao, Sr. Director of Product Management at GitHub

How has the integration of GPT-4 into GitHub Copilot X improved the AI’s understanding of code and its ability to generate logical code suggestions compared to its predecessors?

With GPT-4, we’re seeing even bigger gains in logical reasoning and code generation. The larger model is helping GitHub Copilot understand more of a developer’s codebase to offer more tailored suggestions and better summations of documentation. We believe this is just the beginning.

As GitHub Copilot X is becoming more of an AI assistant throughout the entire development lifecycle, what are some challenges you’ve faced in ensuring seamless integration with the various tools and platforms developers use?

Our R&D team at GitHub Next has been working to move past the editor and evolve GitHub Copilot into a readily accessible AI assistant throughout the entire development lifecycle. We will continue to experiment, innovate, and make improvements over time, and welcome feedback from the community to ensure we’re delivering the most seamless developer experience. For example, this is just the start of our work with GPT-4, and we’ll continue to work with OpenAI to improve the model’s functionality specifically for developers.

Can you share more about the AI-generated tags for pull requests and how they can benefit developers in managing and understanding their code changes?

Pull requests are a central part of the GitHub user experience. Copilot for pull requests helps developers write pull request descriptions and tags are automatically filled out by GitHub Copilot based on the changed code. Ultimately, helping their teams review and merge pull requests faster.

A good pull request description can help reviewers turn changes around quickly and empowers the wider team to keep up with progress. However, there are different types of information that developers include in pull request descriptions and the rules vary between teams. In order to meet developers where they are, we’ve built a feature that allows developers to insert marker tags in their pull request descriptions. When the description is saved, Copilot will expand the marker into a description of the changes in the pull request. Developers can then review or modify the suggested description.

How does GitHub plan to address concerns around AI-generated code, such as potential bias, security vulnerabilities, and intellectual property issues?

Since we launched Copilot, we’ve continued to make improvements to deliver better code suggestions, improved responsiveness, and heightened security. GitHub Copilot automatically blocks common insecure code suggestions by targeting issues such as hardcoded credentials, SQL injections, and path injections. More details can be found in this blog post.

Like any other coding tool, you should always use GitHub Copilot together with human code review, testing practices, security tools, and other best practices.

We’ve been committed to innovating responsibly with GitHub Copilot from the start and will continue to invest in the AI-powered developer experience of the future. We’re introducing new capabilities to GitHub Copilot to continue delivering responsible innovation, including the ability to identify strings matching public code with a reference to those repositories. Furthermore, we are committed to making sure AI systems are developed responsibly and in ways that warrant people’s trust. More details on the principles we’re committed to with GitHub Copilot can be found in our FAQ here.

With the introduction of Copilot Voice, how do you envision the role of voice commands in the software development process, and what challenges do you foresee in implementing this technology?

GitHub Copilot Voice enables voice-based interaction with GitHub Copilot, enabling the benefits of an AI pair programmer while reducing the need for a keyboard. With the power of your voice, we’re excited about the potential to bring the benefits of GitHub Copilot to even more developers, including developers who have difficulty typing using their hands. GitHub Copilot Voice only reduces the need for a keyboard when coding within VS Code for now, but we hope to expand its capabilities to other integrated developer environments (IDEs) through further research and testing.

What impact do you expect GitHub Copilot X to have on the developer community, especially in terms of education and skill development for new and experienced developers?

Copilot X is our vision for the future of AI-powered software development. With AI available at every stage of the development lifecycle, repetitive and mundane tasks are removed, enabling developers to focus on the more creative parts of the job, ultimately making developers happier.

GitHub Copilot is already writing 46% of code and helps developers code up to 55% faster. This is just the beginning. With a new generation of more productive, fulfilled, and happy developers, the knock on impact for businesses is better software, faster product delivery and greater innovation.

We also believe that GitHub Copilot has the potential to lower barriers to entry, enabling more people to explore software development, and join the next generation of developers.

As GitHub Copilot X learns from an organization’s codebase and documentation, how do you ensure the privacy and security of proprietary information while still providing personalized AI assistance?

We are committed to making sure AI systems are developed responsibly and in ways that warrant people’s trust. More details on the principles we’re committed to with GitHub Copilot can be found in our FAQ here.

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