Large Language Models (LLM) have enabled machines to write code. The resulting movement, AI-assisted coding, promises to improve the productivity of software developers. However, AI-assisted coding is still in its infancy. This implies that we should embrace it with caution, guardrails, and realistic expectations.
This talk presents both the short- and long-term implications of using AI assistants to write code. We do so based on extensive CodeScene research analyzing over 100k AI refactorings in real-world codebases. Based on this data, we debunk the productivity claims of today’s AI assistants; it is easy to mistake code-writing speed for productivity.
We then step out on a new path, showing how the same line of research introduces a revolutionary technology for supporting auto-generated code improvements. Using real-world demos, you will see the power of AI-assisted coding without the risks as we automatically improve existing code. In conclusion, we explore how these novel tools not only address industry challenges such as technical debt but also underscore the growing significance of comprehending code over mere writing in the age of AI.
Video producer: https://devoxx.gr/