This study compares framework suitability across three classes of enterprise generative AI applications: a documentation-based development assistant, an email and inquiry automation system, and an Oracle-to-PostgreSQL migration tool. The analysis evaluates component boundaries, orchestration, policy retrieval or reason...
Oleg Grynets, Olena Pochernina, A. Seletska et al.· 0 citations
Findings show that the proposed deterministic-first architecture can preserve an auditable structural baseline, quantify observed primary-key and relationship gaps, and prevent model-generated hypotheses from being silently promoted to source-grounded architectural facts.
Oleg Grynets, Olena Pochernina, V. Lyashkevych· 0 citations
Large language model (LLM)-based database migration is often treated as direct code transformation, although enterprise Oracle systems contain heterogeneous SQL and PL/SQL artifacts with different dependencies, execution order, complexity, and validation needs. This paper proposes a hybrid dependency-aware framework th...
Oleg Grynets, O. Kaskun, A. Seletska et al.· 0 citations
This study investigates whether a frontier LLM can generate Dockerfiles and Docker Compose configurations for multi-service applications using repository contents without access to developer-authored deployment artifacts and analytically derives a minimal explicit deployment specification for information that cannot be...
Oleg Grynets, Kyrylo Fursov, V. Lyashkevych et al.· arXiv.org· 1 citation
The results show that specification size alone does not predict implementation quality and that cross-agent transfer can produce substantial agent-dependent degradation, and suggest that specifications in heterogeneous SDD workflows should not automatically be treated as agent-neutral artifacts.
Oleg Grynets, O. Ilchuk, Dariia Zatulna et al.· 1 citation
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