We present a coverage-driven framework that optimizes RISC-V smoke regressions by decomposing VCS coverage into feature-specific subsets via tag-based pattern matching, ranking tests via greedy set cover, and flagging runtime outliers. Applied to a 978-test production suite drawn from a larger regression pool of 10,000 tests, the framework cut smoke tests by 40% and peak test runtime by 63%, while improving coverage on key architectural features-including +64% (SMRNMI), +53% (timer), and +25% (counters)-with modest regressions on a few features (median <3%), all within project thresholds.

Verifying modern RISC-V cores requires qualifying every merge request (MR) against a large and evolving test space spanning ISA extensions, micro-architectural features, and system-level scenarios. Manually selecting appropriate tests for each MR is time-consuming and error-prone, and does not scale with the rate of RTL changes. This work presents an AI-driven testlist generator that automatically derives MR-specific regression lists for a production RISC-V core verification environment. The tool analyzes Git diffs for an MR, infers impacted features using a combination of static rules and large language models (LLMs), and synthesizes targeted regressions across multiple test generators. The resulting flow reduces MR-qualification effort, improves repeatability, and provides a concrete path toward coverage-driven, closed-loop test selection for RISC-V core verification.