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

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.