Real World Coding with Copilot

GitHub's Copilot is still in its first year, but is presents a once in a generation opportunity for engineering and product development managers, but it has to be rolled out strategically and with realistic expectations.

Before I get too far, GitHub Copilot is not the same as Microsoft's general AI tool also named Copilot. GitHub Copilot is an AI-powered code completion tool designed to assist developers by suggesting whole lines or blocks of code as they type. It is built on OpenAI's Codex model and integrates with popular development environments. Copilot adapts to a user's coding style over time, supports multiple programming languages, and can generate tests and reusable code segments. This tool aims to enhance coding efficiency, reduce repetitive work, and improve code quality, thereby boosting overall developer productivity and job satisfaction.

If you ask GitHub how effective Copilot is, they will claim it can increase developer productivity by 55%, speed up code reviews by 15%, and improve developer satisfaction by 70%. Other real world research shows productivity gains around 10% to 20% . Whatever the case may be, this is a huge gain. Take an engineering team of 300 people. The effective roll out of Copilot gives a software development team the same productivity gains as adding around 60 new people. From a budget owners perspective, this is a huge deal considering a GitHub Enterprise license only runs around $50/mo/developer.

Possibly most exciting for a student of Total Quality Management (TQM) for software development, Copilot can write unit and integration tests. I have personally found that the art of automated testing is often one of the more difficult skills to teach more junior developers. All too often this resulted in surprise breaks to the code later and everyone asking why the Continuous Integration stack failed to catch an issue?

GitHub Copilot significantly impacts the process of writing unit and integration tests by offering intelligent, context-aware suggestions. It simplifies the task of creating tests by generating test cases based on the code and comments provided by the developer. This feature not only speeds up the test-driven development process but also helps in ensuring comprehensive coverage, leading to more robust and reliable software. The tool's ability to understand and respond to specific test-related queries enhances the efficiency and accuracy of test writing, making it a valuable asset in the software testing and quality assurance process.

Cautions

While a utopia of more productive developers sounds great, it's not without its areas of caution.

The security and intellectual property implications of GitHub Copilot, which involves transmitting code to its servers, are noteworthy. Concerns arise around the confidentiality and privacy of the code, especially for proprietary or sensitive projects. The potential for inadvertent exposure of proprietary algorithms or business logic is a significant risk. Organizations should consider these factors, particularly in scenarios involving confidential or proprietary information, and establish protocols to manage what code is shared with Copilot. Additionally, it's important to review GitHub's policies regarding data handling and privacy to understand how transmitted code is managed and protected.

Over-reliance on GitHub Copilot can diminish a developer's understanding of code and syntax. This dependency risks eroding fundamental programming skills, making developers less capable when the tool is unavailable. A developer noted frustration when Copilot was not running, highlighting the challenge of increased reliance on the tool.

GitHub Copilot may not always understand the broader context of a project, potentially leading to suggestions that don’t fit the intended purpose. This issue is compounded if the starting code has flaws, as Copilot might unintentionally amplify these errors, as noted by a developer.

Using Copilot in scenarios involving critical security code, proprietary projects, or complex, unique problems is risky. In these situations, the stakes are too high for relying on automated suggestions, and human expertise is more reliable.

Copilot, like any AI tool, may have inherent biases. Its suggestions can also vary between developers, potentially leading to inconsistencies in a codebase. Additionally, the environmental impact of using AI tools is a growing concern.

Effective Rollout of Copilot

Copilot is a tool, and like any tool, you cannot just throw it out to your entire development team and expect magic. Using the following approach will help to ensure you get the most of your investment.

• Introduction and Training: Start with an introductory session explaining GitHub Copilot's capabilities, focusing on its role as a tool rather than a solution. Emphasize the importance of understanding and maximizing its use.
• Workflow Integration: Encourage developers to incorporate Copilot into their daily coding practices. This includes using it for code completion and leveraging its ability to understand and suggest context-based code from comments.
• Contextual Use: Train developers to provide clear context and intent in their code and comments, which helps Copilot generate more accurate suggestions.
• Prompt Crafting: Educate the team on effective prompt crafting, which involves clear, specific, and detailed comments and code to guide Copilot’s suggestions.
• Best Practices: Implement best practices such as describing the coding goal at the beginning of files, being flexible with suggestions, providing examples for complex tasks, and following good naming conventions.
• Feedback and Adaptation: Encourage developers to provide regular feedback on their experience with Copilot. Use this feedback to adapt the tool's usage and improve integration with existing workflows.
• Review and Refinement: Regularly review the code generated by Copilot for quality assurance, ensuring adherence to organizational standards and practices.
• Continuous Learning: Keep the team updated on new features and best practices in using Copilot, fostering a culture of continuous learning and adaptation.
• Evaluation and Metrics: Evaluate the impact of Copilot on productivity, code quality, and developer satisfaction periodically to measure its effectiveness and ROI.

Cultural Changes

No matter how impressive a technology is, leaders need to consider how to manage the cultural implications of the new tools. I have found the Risk, Habit, Identity, and Power (RHIP) framework the best change management tool for cases like this. Using this framework for change to roll out GitHub Copilot in an organization involves:

• Risk: Address potential concerns about dependency on automation and code quality. Ensure developers understand Copilot's role as an aid, not a replacement for their skills.
• Habit: Integrate Copilot into daily coding routines. Start with simple tasks, gradually increasing complexity as comfort grows, encouraging regular use and familiarity.
• Identity: Reinforce the identity of developers as innovators and problem solvers. Position Copilot as a tool that enhances these skills rather than diminishing them.
• Power: Empower developers with training sessions on Copilot's advanced features. Encourage them to contribute to its evolution by providing feedback, fostering a sense of ownership and influence over the tool's development.

This approach ensures a balanced and systematic introduction of Copilot, aligning with the organization's culture and developers' professional growth.

Conclusion

GitHub Copilot offers significant potential for enhancing productivity and quality in software development. However, its implementation requires careful and strategic planning. While promising remarkable efficiency gains, it's crucial to be aware of the associated security, intellectual property, and reliance risks. A systematic rollout, coupled with training, workflow integration, and ongoing evaluation, is essential. Moreover, managing the cultural changes with frameworks like RHIP ensures that Copilot's integration supports and enhances the developers' roles, skills, and identities. With prudent management and strategic implementation, Copilot can be a transformative tool for development teams.