# The report

## The default report

The default report (see sample) consists of an experiment summary at the top, followed by the summary of each benchmark.

### Experiment summary

At the top of the report we use a critical difference diagram to summarize the experiment. It compares fuzzers over all benchmarks, visualizing average ranks and statistical significance. This diagram and the underlying methodology was introduced by Demsar, and is often used in the field of machine learning to compare algorithms over multiple data sets.

The line in the diagram represents the axis on which the the average ranks of the fuzzers are shown. The average ranks are computed from the medians of the reached coverage of each fuzzer on each benchmarks. (You can see the medians by expanding the “Median coverages on each benchmark” table under the graph.) Lower number in average rank is better (closer to “1st place”). In other words, fuzzers placed more on the left are better.

Groups of fuzzers that are connected with bold lines are *not* significantly different from each other. The *critical difference* (CD) is also shown at the top of the plot, representing how far two fuzzers need to be on the axis to be statistically significantly different. The critical difference is computed based on a post-hoc Nemenyi test performed after the Friedman test.

The pivot table under the critical difference diagram shows the median reached coverage numbers.

### Per-benchmark summary

Below the experiment summary, the report shows the result of each benchmark. The default report show three plots:

- Bar plot of the median reached coverage of each fuzzer in order.
- Violin plot of the distribution of the reached coverages (including min, 25%, 75%, max).
- Coverage growth plot aggregating individual trials (error band shows 95% confidence interval around the mean coverage).

The table under the plots show a statistical summary of reached coverage samples for each fuzzer. This includes the number of trials, mean, median, standard deviation.

Under the table we show a graphical summary of pairwise statistical tests.

The default report includes pairwise tests of effect size and null hypothesis significance. The effect size is determined using the Vargha-Delaney A12 measure and the null hypothesis is rejected with the two-tailed Mann-Whitney U test.

Both methods are recommended by Arcuri et al..

- Green cells in the Vargha-Delaney A12 measure plot indicate the probability that the fuzzer in the row wil outperform the fuzzer in the column. An A12 value of
`0.50`

indicates there is no difference between the two fuzzers being compared, and a value of`1.0`

indicates a 100% probability that the fuzzer in the row will outperform the fuzzer in the column. - Green cells in the Mann-Whitney U plot indicate that the reached coverage (or bugs covered) distribution of a given fuzzer pair is statistically significantly different from each other (α=0.05).

See how to create your own reports under Custom analysis and reports.