Keisuke Nishimura, Yuichi Sugiyama, Yuki Koike, Masaya Motoda, Tomoya Kitagawa, Toshiki Takatera, Yuma Kurogome (Ricerca Security, Inc.)

Fuzzing has contributed to automatically identifying bugs and vulnerabilities in the software testing field. Although it can efficiently generate crashing inputs, these inputs are usually analyzed manually. Several root cause analysis (RCA) techniques have been proposed to automatically analyze the root causes of crashes to mitigate this cost. However, outstanding challenges for realizing more elaborate RCA techniques remain unknown owing to the lack of extensive evaluation methods over existing techniques. With this problem in mind, we developed an end-to-end benchmarking platform, RCABench, that can evaluate RCA techniques for various targeted programs in a detailed and comprehensive manner. Our experiments with RCABench indicated that the evaluations in previous studies were not enough to fully support their claims. Moreover, this platform can be leveraged to evaluate emerging RCA techniques by comparing them with existing techniques.

View More Papers

Enhancing Symbolic Execution by Machine Learning Based Solver Selection

Sheng-Han Wen (National Taiwan University), Wei-Loon Mow (National Taiwan University), Wei-Ning Chen (National Taiwan University), Chien-Yuan Wang (National Taiwan University), Hsu-Chun Hsiao (National Taiwan University)

Read More

Towards Automatically Generating a Sound and Complete Dataset for...

Aravind Machiry (UC Santa Barbara), Nilo Redini (UC Santa Barbara), Eric Gustafson (UC Santa Barbara), Hojjat Aghakhani (UC Santa Barbara), Christopher Kruegel (UC Santa Barbara), Giovanni Vigna (UC Santa Barbara)

Read More

Do Privacy Labels Answer Users' Privacy Questions?

Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

Read More