The default report shows the results of an experiment using plots, ranking methods, and statistical tests that are commonly used in the literature. However, these defaults are not the only way to rank fuzzers, visualize results, or determine statistical significance.
We encourage researchers to look at the data from different points of view as well. We provide a library of alternative analysis, statistical tests and plotting options under the
We invite researchers to contribute their own scripts for various tests and visualization of the data to the library.
You can use the
generate_report.py tool for creating reports. For example, you can re-generate the report from the raw data of our sample experiment like this:
mkdir ~/my-report; cd ~/my-report wget https://www.fuzzbench.com/reports/sample/data.csv.gz PYTHONPATH=<fuzzbench_root> python3 analysis/generate_report.py \ [experiment_name] \ --report-dir ~/my-report \ --from-cached-data
You can find the link to the raw data file at the bottom of each previously published report.
You can generate different types of reports (see available templates). For example, to generate a more detailed report with more analysis results (i.e., multiple ranking methods and statistical tests), use the
--report_type experimental flag. We also encourage you to add your own templates and report types to the library.
Check out the rest of the command line options of the tool with:
PYTHONPATH=<fuzzbench_root> python3 analysis/generate_report.py --help
Another way to do custom analysis is to use Jupyter / Colab notebooks. You can find some example notebooks here.
If you do some custom analysis that might be useful for others as well, please consider adding it to the analysis library!