As codebases grow and include more AI-generated code 🤖, it becomes harder to analyze and maintain them. This session is for experienced developers and team leads who need practical strategies to improve code quality, streamline code reviews, and enhance maintainability.

We'll talk about the challenges of understanding AI-generated code, including hidden bugs 🐛 and security problems 🔒. You'll learn about important measurements to assess the quality, usefulness, and performance of code. {sigh}YEAH YEAH! You are already using them! 😉 {/sigh})

We'll cover specific techniques to analyze the structure of the codebase, identify potential issues, and improve code quality. {sigh}YEAH YEAH! It's already on your CICD {/sigh}

We'll also explore how to get insights from the code about the field's knowledge landscape 🗺️, how the development team works together 🤝, problems 🚧, features ✨, and other things. {giddy}I don't think that one is so popular{/giddy} 🤩

We'll also discuss the pros and cons of using AI-generated code, including increased productivity 🚀 {skeptical} Well it all depends ..{/skeptical} and reduced development time ⏱️ {hmmm} is that really the case??{/hmm}, while acknowledging {kind} potential 😉{/kind} challenges.

Attendees will learn how to manage and improve their code and will leave with real-world examples and actionable strategies! 💪

This session proposal focuses on practical strategies for analyzing and improving large, growing codebases that include AI-generated code. The fast addition of AI-generated code presents challenges such as unknown quality, potential blind spots, and increased security and conformance risks.

This session will address these concerns by providing essential metrics and techniques for identifying and mitigating potential problems and ensuring that AI-generated code integrates seamlessly with existing systems. The session will also cover methods for extracting insights from the codebase regarding domain knowledge, team dynamics, bugs, features, and more.

Outline
Introduction (5 minutes)
Challenges of AI-Generated Code (10 minutes)
Essential Metrics for Code Quality (10 minutes)
Analyzing Codebase Structure (10 minutes)
Improving Code Quality and Maintainability (10 minutes)
Extracting Insights from the Codebase (10 minutes)
Benefits and Risks of AI-Generated Code (5 minutes)
Q&A and Discussion (10 minutes)