coverage-analysis

Code coverage during a fuzzing campaign is measured by compiling the target with LLVM or GCC instrumentation flags and post-processing the profile data using tools like llvm-cov or gcovr to generate detailed execution reports.

You can identify fuzzing blockers like magic value checks by analyzing code coverage reports to find conditions that prevent fuzzers from reaching deeper code paths, indicating where to improve harnesses or add dictionary entries.

To generate an HTML coverage report from LLVM profile data, compile your C++ code with LLVM coverage instrumentation and post-process the collected profile data using the llvm-cov tool to visualize executed lines and branches.

Tracking fuzzing progress over time is best done by periodically generating code coverage reports to measure the percentage of exercised lines and branches, helping assess harness effectiveness and identify untested code areas.

Yes, analyzing fuzzing coverage requires compiling the target code with specific LLVM or GCC instrumentation flags to enable profile data collection for post-processing with tools like gcovr or llvm-cov.

Your fuzzer may stop finding crashes because only a small percentage of the target code is being exercised; generating a coverage report helps identify untested areas and fuzzing blockers that require harness improvements.