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Semantic correctness in LLM-generated multi-level models is only partially achieved, revealing significant gaps in their understanding of complex semantics.
Multi-level modelling slashes post-change inconsistencies by unifying artefacts, outperforming traditional two-level modelling in software maintenance scenarios.
Few-shot prompting boosts syntactic quality of LLM-generated model transformation languages, but semantic correctness remains a challenge.
Bounded Context violations in DDD projects are shown to significantly hinder maintenance quality, revealing a critical challenge for practitioners.
Forget monolithic LLMs for requirements engineering: QUARE's multi-agent negotiation framework achieves 105% better compliance by explicitly surfacing and resolving inter-quality conflicts.
