➀ The paper compares UVM and Python-based Cocotb for AES hardware verification, showing Cocotb's 89.55% code coverage vs. UVM's 87.49%, but raises questions about UVM's low functional coverage (47/64 cases) without clear explanations;

➁ UVM demonstrated faster simulation time (1000ns vs. Cocotb's 10,000.5ns), though experts note Cocotb's flexibility with Python libraries and simpler synchronization offer workflow advantages for early RTL development;

➂ Cadence's Paul Cunningham highlights UVM's commercial EDA tool integration and constraint solver optimizations, while Raúl Camposano observes Cocotb's growing relevance in AI-driven verification ecosystems despite the paper's methodological limitations.