All unit tests should be (1) intentionally designed for coverage, (2) non-redundant, and (3) maintainable. Apply these rules: 1) Enforce correctness with invariants - When the system under test has known relationships, assert them directly and return a clear error code on violation. - Example (CPU topology): ```cpp int cpucount = ncnn::get_cpu_count(); int bigcpucount = ncnn::get_big_cpu_count(); int littlecpucount = ncnn::get_little_cpu_count(); if (bigcpucount + littlecpucount != cpucount || bigcpucount > cpucount || !(littlecpucount < cpucount)) return -1; ``` 2) Cover critical tensor ranks/shapes, not just “typical” ones - If a layer/operator supports multiple ranks or expects specific dimensionalities, add targeted tests for the missing ranks (e.g., 1D and 4D when relevant). - Make rank coverage a checklist item when adding/adjusting operator tests. 3) Reuse shared test helpers for layer/model execution - Prefer existing generic helpers (e.g., a `test_layer<>`-style utility) instead of duplicating manual layer creation, parameter loading, pipeline creation, and forward logic. - Only write bespoke setup when the helper cannot express the needed test scenario. 4) Remove duplicated or near-identical test cases - Deduplicate test vectors/case lists. If new cases are added, ensure they add distinct coverage (different shapes/parameters/edge conditions), rather than repeating the same configuration. - If duplication is discovered, consolidate into a parameterized list or a shared test case generator. Result: tests become smaller, faster to review, easier to maintain, and less likely to miss regressions due to missing shape variants or redundant cases.