## 5 Evaluation
### Experimental Results and Analysis  
After 3600 CPU hours of testing, we obtained the results shown in Table 3. The unpaired \(t\)-test was selected by us for evaluating significance. We hypothesized that the results of each fuzzing framework on each tested program followed a Gaussian distribution. Additionally, we assumed that for each tested program, the results were sampled from a population with the same standard deviation. The significance level is represented by \"*\", where \"ns\" indicates a \(p\)-value \(>=\) 0.05, \"*\" represents a \(p\)-value \(<\) 0.05, \"***\" represents a \(p\)-value \(<\) 0.01, \"***\" represents a \(p\)-value \(<\) 0.0001, and \"***\" represents a \(p\)-value \(<\) 0.0001.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Program** & **Test Driver** & **Input Format** & **Option** \\ \hline
**libpng** & pngimage & PNG & @ @ \\
**libjpeg-turbo** & djpeg & JPEG & @ @ \\
**jasper** & jasper & JP2 & \(\
eg\)input @@-output-format jp2 \\
**guetzli** & guetzli & JPEG & @ @/dev/null \\
**Binutils** & readelf & ELF & \(\
eg\)qteSdcWw\(\
eg\)dyn-syms-D @ @ \\
**Binutils** & nm-new & ELF & \(\
eg\)-A-C-l\(\
eg\)synthetic @ \\ \hline \end{tabular}
\end{table}
Table 2: Programs used for testing, their requisite input formats, and the parameters employed during fuzzing.  
_OEF compared with AFLTeam_: The AFLTeam and the original ensemble fuzzing framework (OEF) are the two fundamental works of our task allocation-based ensemble fuzzing framework (TAEF). From the experimental results, it is evident that these two fuzzing frameworks excel in different areas. However, overall, the performance of OEF is better than AFLTeam. Indeed, OEF outperformed AFLTeam on four out of six tested programs, with a maximum improvement exceeding 10%; and the decrease ratios on the other two lagging tested programs did not exceed 5%. This suggests that ensemble fuzzing indeed can leverage the strengths of all of the paralleled fuzzers to achieve better coverage than the multiple instance parallelism of a single fuzzer. Thus, in theory, applying the task division method to ensemble fuzzing should yield better results than applying it to parallel fuzzing with a single fuzzer (such as AFLTeam).  
_TAEF compared with AFLTeam_: The obtained results align with the theory discussed earlier. Our task allocation-based ensemble fuzzing framework (TAEF) achieved higher branch coverage than AFLTeam in all six tested programs. Overall, TAEF covered an average of 8.9% more branches than AFLTeam. Particularly, there was a significant improvement of over 24% in the tested program named nm-new. Moreover, the improvements in our framework compared to AFLTeam in five out of the six tested programs show significance. This indicates that applying the task division strategy to the ensemble fuzzing is more advantageous than applying it to parallel fuzzing with a single fuzzer and should be a future direction of development.  
_TAEF compared with OEF_: It can be seen that our task allocation-based ensemble fuzzing framework (TAEF) achieved a higher branch coverage than the original ensemble fuzzing framework (OEF) in five out of the six tested programs. Overall, TAEF covered 4.1% more branches than OEF. Notably, the most significant improvement was observed in the program called nm-new, with an increase of over 13%. Although the average coverage improvement in our framework compared to OEF was not substantial, all of the improvements in these five tested programs were statistically significant. This indicates that our framework outperforming OEF in terms of branch coverage is not by coincidence. The limited increase in coverage may be attributed to the fact that OEF already achieved near-saturation coverage in these programs, making further improvement challenging. In the only program where TAEF has a lower branch coverage than OEF, the decrease of less than 1% is not statistically significant. In conclusion, our framework indeed enhances the efficiency of ensemble fuzzing, aligning with our initial objectives.