Collaborative Research: SHF: Medium: Reinventing Fuzz Testing for Data and Compute Intensive Systems

新兴的数据密集型和计算密集型软件应用程序的重要性继续以越来越快的速度增长。云计算框架通过提供随时可用的资源，使开发和运行大数据应用程序变得更加容易。计算机架构的最新趋势包括异构性和专业化以提高性能，例如基于FPGA构建的硬件加速器。虽然模糊测试已经成为检测传统软件应用程序中的正确性和性能缺陷的有效技术，但由于其长延迟，它不容易适用于数据密集型和计算密集型应用程序。随着这些数据和计算密集型应用被集成到关键任务系统中，其鲁棒性和正确性成为最优先考虑的问题，本研究将自动化模糊测试成功地应用到大数据应用和异构应用领域。这项研究正在产生一套开源测试工具，以提高整体应用程序质量，转化为资源，时间和成本的节省。它有三个创新的组成部分：（1）新的输入突变技术和测试延迟减少方法的数据密集型应用程序，（2）有效的指导指标和反馈监控方法的异构计算应用程序，和（3）新的性能感知模糊策略，以诱导数据偏斜，计算偏斜，和内存偏斜。该研究旨在使在数据密集型和计算密集型领域编写软件的软件工程师、数据科学家和定量分析师受益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The importance of emerging data-intensive and compute-intensive software applications continues to grow at an increasing rate. Cloud-computing frameworks make it easier to develop and run big data applications by providing readily available resources. Recent trends in computer architectures incorporate heterogeneity and specialization to improve performance, such as hardware accelerators built on FPGAs. While fuzz testing has emerged as an effective technique for detecting correctness and performance defects in traditional software applications, it is not easily applicable to data-intensive and compute-intensive applications due to their long latency. Now that such data- and compute-intensive applications are being integrated into mission-critical systems, their robustness and correctness are of the highest priority.This research brings the success of automated fuzz testing to the domain of big data applications and heterogeneous applications. This research is producing a suite of open-source testing tools to improve overall application quality, translating into resource, time, and cost savings. It has three innovative components: (1) new input-mutation techniques and testing latency reduction methods for data-intensive applications, (2) effective guidance metrics and feedback-monitoring methods for heterogeneous computing applications, and (3) new performance-aware fuzzing strategies to induce data skews, compute skews, and memory skews. This research aims to benefit software engineers, data scientists, and quantitative analysts who write software in data-intensive and compute-intensive domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.