Test-Input Generation for Tensor Programs: What Actually Finds Kernel Bugs
Test-input generation for tensor kernels is folkloric. Most projects pick a representative shape and dtype, run a fixed-shape allclose-style check, and ship. We make the choices explicit and measure them. Using the gpuemu op-schema-aware seeded fuzzer (arXiv:2606.20128), we evaluate seven test-generation strategies across a 26-op corpus (16 correct controls and 10 LLM-style buggy variants seeded with documented transcription patterns) on an RTX 3060 GPU instance. Strategies vary the shape candidate set, the dtype mix, and the input value distribution. We report each strategy on two axes: bug recall and control false-positive (FP) rate. Boundary-only shape sampling is the operationally saf...
| Authors | Dipankar Sarkar |
|---|---|
| Primary category | cs.SE |
| Published | 23 June 2026 |
| Last updated at source | 21 July 2026 |
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