AI Writing Our Code: Real Experience with GitHub Copilot and AI-Assisted Development

Overview

Explore the real-world impact of GitHub Copilot and AI-assisted coding, from performance expectations to ethical considerations and practical workflow integration strategies.

Syllabus

• Introduction to AI-Assisted Development
Overview of AI in Software Engineering
Introduction to GitHub Copilot
Historical context and evolution of AI in coding
• Setting Up GitHub Copilot
Installation and Configuration
Supported Environments and Languages
Initial Setup and Personalization
• Understanding Copilot's Capabilities
Code Completion Features
Handling Documentation and Code Comments
Integrating with Existing Workflows
• Practical Workflow Integration
Best Practices for Using Copilot
Integrating Copilot with CI/CD Pipelines
Real-world Use Cases and Examples
• Enhancing Developer Productivity
Efficiency Gains and Time Management
Reducing Boilerplate and Repetitive Tasks
Collaborative Coding with AI Assistance
• Evaluating Performance and Limitations
Accuracy and Reliability Assessment
Impact on Code Quality and Readability
Limitations and Common Pitfalls
• Ethical Considerations in AI-Assisted Coding
Intellectual Property and Code Ownership
Bias and Fairness in AI-generated Code
Privacy Concerns and Data Security
• Case Studies and Applications
Success Stories from the Industry
Comparative Studies on Development Speed and Quality
Future Potential and Emerging Trends
• Hands-On Projects
Building a Project with GitHub Copilot
Pair Programming with AI
Writing Tests and Debugging with Copilot
• Final Reflections and Future Directions
Summary of Key Learnings
Discussion on the Future of AI in Development
Optional Paths for Further Study and Exploration

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Subjects

Programming