一句话：提出 BigFuzz，将模糊测试应用于大数据分析，以应对 DISC 系统环境启动慢、覆盖率扩展难和变异数据无意义三大障碍。
随着大数据分析日益流行，数据密集型可扩展计算（DISC）系统用于处理大规模数据，但针对此类数据应用的自动化测试十分困难：数据往往不完整、持续演化且难以预先知晓。将传统模糊测试直接用于大数据分析并不简单，原因有三：DISC 系统延迟高，朴素模糊测试会把 98% 的时间花在环境搭建上；传统分支覆盖率难以扩展到 DISC 应用，因为多数二进制代码来自 Apache Spark 等框架实现；随机比特或字节级变异很难生成有意义的数据，因而无法揭示真实应用缺陷。论文提出 BigFuzz，目标是在这一场景中实现有效模糊测试，但摘要未报告其具体方法细节与实验结果。
As big data analytics become increasingly popular, data-intensive scalable computing (DISC) systems help address the scalability issue of handling large data. However, automated testing for such data-centric applications is challenging, because data is often incomplete, continuously evolving, and hard to know a priori. Fuzz testing has been proven to be highly effective in other domains such as security; however, it is nontrivial to apply such traditional fuzzing to big data analytics directly for three reasons: (1) the long latency of DISC systems prohibits the applicability of fuzzing: naïve fuzzing would spend 98% of the time in setting up a test environment; (2) conventional branch coverage is unlikely to scale to DISC applications because most binary code comes from the framework implementation such as Apache Spark; and (3) random bit or byte level mutations can hardly generate meaningful data, which fails to reveal real-world application bugs.