Update README.md

Author: moratorium08

README.md

@@ -112,6 +112,7 @@ To fuzz each PUT, move to the corresponding subdirectory and run one of the foll
 
 
 The results of the FuzzBench and Magma benchmark in our paper can be downloaded from [here](https://drive.google.com/drive/folders/1WtEolT0AuH535Bbhv07Lyo8zUYnrXnB4). The results of 4.3 (bandit algorithm comparison) is also included in `slopt_fuzzbench_plot_data.tar.gz`.
+Note that the size of the result of Magma is so large (about 65GB) that we prepared in the same directory lists of bugs that were found during our experiments so that you can check our results instantly. This list was generated by using [exp2json.py](https://hexhive.epfl.ch/magma/docs/technical.html#exp2jsonpy).
 To fully reproduce our results, a considerable amount of computing resources is required. For FuzzBench, we used 1 [day] x 1 [CPU] x 6 (fuzzers) x 10 (PUTs) x 30 (instances) = 1800 [CPU x day]. For MAGMA, we used 1 [d] x 1 [CPU] x 6 (fuzzers) x 21 (PUTs) x 10 (instances) = 1260 [CPU x day]. Note that, moreover, we took benchmarks with AMD EPYC 7742 2.25GHz CPU, which may run somewhat faster than ordinary CPUs used in clouds although we ran 110 fuzzer instances in parallel. To check whether it is faster, please compare the `execs_per_sec` column of `plot_data` in `slopt_fuzzbench_plot_data.tar.gz`.
 
 # Citation
@@ -134,5 +135,5 @@ To fully reproduce our results, a considerable amount of computing resources is
 # Reference
 
 - [1] Siddharth Karamcheti, Gideon Mann, and David S. Rosenberg. 2018. Adaptive Grey-Box Fuzz-Testing with Thompson Sampling. In Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security. ACM, 37–47. https://doi.org/10.1145/3270101.3270108
-- [2] Xiajing Wang, Changzhen Hu, Rui Ma, Donghai Tian, and Jinyuan He. 2021. CMFuzz: context-aware adaptive mutation for fuzzers. Empirical Software Engi- neering 26, 1 (2021), 10. https://doi.org/10.1007/s10664-020-09927-3
+- [2] Xiajing Wang, Changzhen Hu, Rui Ma, Donghai Tian, and Jinyuan He. 2021. CMFuzz: context-aware adaptive mutation for fuzzers. Empirical Software Engineering 26, 1 (2021), 10. https://doi.org/10.1007/s10664-020-09927-3
 - [3] Mingyuan Wu, Ling Jiang, Jiahong Xiang, Yanwei Huang, Heming Cui, Lingming Zhang, and Yuqun Zhang. 2022. One Fuzzing Strategy to Rule Them All. In Proceedings of the 44th IEEE/ACM International Conference on Software Engineering. IEEE, 1634–1645. https://doi.org/10.1145/3510003.3510174