Technical Debt Management and Refactoring Questions

Direct answer
Audit the 5,000-case suite by scoring each test on defect-detection history and usage/execution frequency, prune anything that's fired zero real defects and hasn't run in the last N cycles, and migrate only the highest-value subset to automation first, since automating everything indiscriminately just moves the maintenance burden rather than reducing it.

Structured elaboration

1. Audit and score: for each test case, pull defect history (how many real bugs has this test ever caught) and execution frequency (is it even run regularly, or has it been effectively abandoned); a test that's never caught a real defect and hasn't run in months is a strong prune candidate regardless of how it looks on paper.
2. Categorize: high-value (frequently run, has caught real defects) automate first; medium-value (occasionally run or occasionally useful) automate in a second wave; low/no-value (never run, never caught anything, or duplicates another case) prune outright.
3. Prioritize migration by risk-weighted usage: automate test cases covering the highest-traffic, highest-change-frequency user flows first, using the same risk-based prioritization logic used for other debt items, rather than migrating in arbitrary or alphabetical order.
4. Governance to prevent re-growth: require a defect-detection or usage justification for any NEW manual test case added going forward, and a periodic (quarterly) re-audit using the same scoring criteria, so the suite doesn't silently regrow to 5,000 low-value cases again within a year.

Worked example
Of 5,000 cases, an audit finds 1,200 have never caught a defect and haven't run in over a year (prune candidates), 2,800 are duplicative variations of a smaller set of core scenarios (consolidate to roughly 600 distinct scenarios), and 1,000 are genuinely high-value, frequently-run, defect-catching cases. Timeline: prune the 1,200 immediately (low risk, clear signal); consolidate the 2,800 duplicates over 6 weeks, since that requires careful verification that no unique coverage is lost in the consolidation; automate the resulting ~1,600 high-value cases (the original 1,000 plus the 600 consolidated) over the following quarter, prioritized by traffic and change frequency.

Trade-offs & pitfalls
The risk in aggressive pruning is discarding a rarely-triggered but genuinely important edge-case test; mitigate by having a domain expert spot-check the prune list before deletion, not just the automated score, since defect-history data can miss a case that's important but has simply never yet caught a bug purely by luck.

Direct answer
Test debt is the accumulated gap between the tests a codebase needs and the tests it has: missing coverage, brittle or flaky tests, and tests that pass without meaningfully verifying behavior. Two fast, practical tactics to reduce it: automate the highest-risk paths first rather than chasing a blanket coverage number, and enforce a test-pyramid shape (many fast unit tests, fewer integration tests, very few slow end-to-end tests) so the suite stays fast enough that people don't start skipping it.

Structured elaboration

1. Targeted automation of critical paths: rank untested code by a combination of change frequency and business criticality (a checkout flow beats an admin settings page), and automate tests there first; this delivers risk reduction per engineer-hour far faster than chasing overall coverage percentage.
2. Enforce the test pyramid: audit the current suite's shape (count of unit vs integration vs end-to-end tests and their respective run times); if end-to-end tests dominate, they're slow and flaky by nature, which erodes trust in the whole suite and is itself a source of NEW test debt as people start skipping or ignoring red builds.

Deciding what to automate first: score candidate areas on (a) how often the code changes (higher change frequency = higher risk of a regression slipping through) and (b) business criticality (revenue-path or compliance-relevant code outranks internal tooling), and start with the intersection of both being high.

Worked example
A codebase's untested areas: an admin-only reporting page (rarely changes, low criticality) and the checkout discount-code logic (changes almost every sprint, directly revenue-relevant). Despite the admin page having zero coverage and the checkout logic having partial coverage, the checkout logic is the higher-priority automation target, since its change frequency and criticality both point to it as the place most likely to regress silently and cause real damage.

Trade-offs & pitfalls
Chasing a blanket coverage percentage target ("get to 80% everywhere") often produces the OPPOSITE of risk reduction: teams write low-value tests against low-risk code to move the number, while the highest-risk, highest-change-frequency code stays under-tested because it's harder to test well. Targeted, risk-based automation is slower to show a clean percentage but faster to reduce real incident risk.