AI-powered code generation tools like GitHub Copilot and Cursor AI have changed software development by making coding faster and automating repetitive tasks. However, these tools still struggle to understand the full context of real-world software projects. They often miss important details, such as security, efficiency, and maintainability, leading to code that works but is not always the best solution. To fix this, we need to add software engineering best practices to AI models and improve how humans and AI work together. This will help AI-generated code meet the real needs of developers and businesses. I will talk about the challenges of AI code generation and suggest ways to make it better for real-world use.

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University. She works at the intersection of AI, Software Engineering, and Security. She received her Ph.D. degree from the University of Texas, Austin. She has received the IEEE TCSE Rising Star, NSF CAREER, IBM faculty, and VMWare Faculty awards. She is also an Amazon Visiting Academic. Her research has been acknowledged by many Distinguished Papers awards including FSE’17, ASE’22, ISSTA’23, the ICSME Most Influential Paper award, and has also been published in CACM Research Highlights and has been widely covered in trade media.