How Large‑Model AI Is Transforming Software Development Tools

Exploring Large‑Model AI in R&D

Today’s discussion focuses on the development and application of large‑model technology in research and development. While large models already assist in code writing, the question is whether they can fundamentally redesign workflows and critical processes.

Outline of the Presentation

Intelligent R&D tool development

Enterprise adoption experiences

Developer practice

Q&A session

Intelligent Development Tools: Evolution and Direction

The journey begins with a review of the evolution of intelligent development tools. Early examples include GitHub Copilot, launched about two and a half years ago, which initially offered automatic code‑completion suggestions while developers typed.

Over time, Copilot’s capabilities expanded; the recent Copilot Chat plugin now supports language translation, unit‑test generation, and more. As large models advance, products rapidly evolve from simple assistance to collaborative partners that can take on entire tasks.

Code generation has shifted from merely completing snippets to generating full solutions based on requirements, effectively turning coding into a service for the entire engineering pipeline. For instance, Copilot now helps users set up unit‑test environments, moving beyond pure code writing.

“Go Fat”: Scaling Code Completion

“Go Fat” refers to the increasing scale of code‑completion. Traditional completion offers a gray suggestion line based on model inference, reducing the time to type a few characters to a single Tab press. Modern tools aim to expand this capability while preserving user habits, delivering larger, more context‑aware completions.