ohos-test-fuzz-generation

You can generate libFuzzer fuzz tests for C++ APIs by providing the target header path and class name. The tool analyzes class methods, creates a fuzz driver skeleton, and constructs parameter logic to produce a standardized fuzz project.

Semantic corpus seed generation improves fuzz testing by producing meaningful initial corpus data based on parameter types and naming patterns. This targeted approach helps the fuzzer discover valid code paths faster than using empty or random inputs.

Yes, this fuzz testing tool supports OpenHarmony, Linux, and Android build environments. It is designed to integrate with OpenHarmony build ecosystems and common LLVM libFuzzer setups for automated test case generation.

The fuzz driver compliance review runs a 26-rule security check covering complex parameter construction, IPC stub handling, size/data misuse, and safe data handling. It generates a compliance report and can guide automatic fixes for supported rules.

To fix IPC stub testing errors in your generated fuzz driver, run the automated security check tool. It identifies IPC stub handling issues and size/data misuse, then guides automatic fixes to satisfy the fuzz-driver safety rules.

The best way to prepare corpus seeds for C++ fuzz testing is using automated semantic generation. The tool analyzes parameter types and naming patterns to construct meaningful initial corpus data, which improves fuzz efficiency over manual seed creation.