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AI coding agents are reshaping open-source ecosystems by diluting human participation while increasing the burden on code review processes.
Few-shot prompting boosts syntactic quality of LLM-generated model transformation languages, but semantic correctness remains a challenge.
A single dataset bridges the gap between security requirements, architecture, and code, enabling a new frontier in secure software engineering research.
Bounded Context violations in DDD projects are shown to significantly hinder maintenance quality, revealing a critical challenge for practitioners.
