Knowns and Unknowns: Testing AI-Generated Code

**WE USE LUMA FOR EVENT SIGNUP: [REGISTER HERE](https://luma.com/ajug-uuog)**[ ](https://luma.com/ajug-uuog) # ​**Knowns and Unknowns: Testing AI-Generated Code** ​ ​Let's face it, AI-assisted coding is rapidly becoming the primary way applications are being written. The power of writing a complex application or feature with just a few commands is too alluring to pass up. ​But between the imprecise nature of language and LLMs' ability to "hallucinate", this creates many opportunities for bugs and unintended behavior to be introduced. Just telling an AI coding agent to "write tests" isn't enough to build confidence in an application's correctness, either. ​In this presentation, we will review three distinct phases of testing AI-written code. The known knowns: using spec-driven development to define the business-meaningful behavior of your application. Known unknowns: using property-based testing to handle edge cases and avoid overfitting and unintended biases. And finally, unknown unknowns: using static analysis and mutation testing to avoid introducing bugs you didn't think about. ​AI-assisted coding is a rapidly evolving field, but this presentation should provide you with a framework and approach to build confidence when using AI to assist with writing applications.