Zhi Jin

Strong code generation doesn't guarantee effective requirement clarification, exposing a critical flaw in LLM capabilities that could hinder software engineering practices.

Intent-driven rule completion in IoT systems can boost completion rates by 43% while embedding safety and traceability throughout the process.

MAAD not only automates architecture design but also enhances the quality of outputs through a collaborative agent framework and advanced LLM integration.

Ditch the one-size-fits-all code intelligence: modeling individual developer behaviors inside the IDE boosts Q&A accuracy by 33.8%.