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.
翻译:模糊有助于自动识别软件测试场中的错误和脆弱性。虽然能够有效生成崩溃性投入,但通常对这些投入进行手工分析。一些根源分析(RCA)技术已被提议用于自动分析碰撞的根本原因以降低这一成本。然而,由于缺乏关于现有技术的广泛评价方法,实现更精细的RCA技术的悬而未决挑战仍然不明。考虑到这一问题,我们开发了一个端对端基准平台RCABench,该平台可以详细、全面地评估各种目标方案的RCA技术。我们与RCABench的实验表明,以往研究中的评价不足以充分支持其主张。此外,可以通过将这些评价技术与现有技术进行比较,利用这一平台来评估新出现的RCA技术。</s>