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QuanFuzz: Fuzz Testing of Quantum Program
Published in , 2018
In this paper, we present QuanFuzz, a search-based test input generator for quantum program. We define the quantum sensitive information to evaluate test input for quantum program and use matrix generator to generate test cases with higher coverage. First, we extract quantum sensitive information – measurement operations on those quantum registers and the sensitive branches associated with those measurement results, from the quantum source code. Then, we use the sensitive information guided algorithm to mutate the initial input matrix and select those matrices which improve the probability weight for a value of the quantum register to trigger the sensitive branch. The process keeps iterating until the sensitive branch triggered. We tested QuanFuzz on benchmarks and acquired 20% - 60% more coverage compared to traditional testing input generation.
Recommended citation: Jiyuan Wang, Ming Gao, Yu Jiang, Jianguang Lou, Yue Gao, Dongmei Zhang, Jiaguang Sun. (2018)."QuanFuzz: Fuzz Testing of Quantum Program " https://arxiv.org/pdf/1810.10310.pdf
BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction
Published in ASE 20, 2020
We propose a novel coverage-guided fuzz testing tool for bigdata analytics, calledBigFuzz. The key essence of our approach is that: (a) we focus on exercising application logic as opposed to increasing framework code coverage by abstracting the DISC frame-work using specifications. BigFuzz performs automated source to source transformations to construct an equivalent DISC application suitable for fast test generation, and (b) we design schema-aware data mutation operators based on our in-depth study of DISC application error types. BigFuzz speeds up the fuzzing time by 78 to1477X compared to random fuzzing, improves application code coverage by 20% to 271%, and achieves 33% to 157% improvement in detecting application errors. When compared to the state of the art that uses symbolic execution to test big data analytics, BigFuzz is applicable to twice more programs and can find 81% more bugs.
Recommended citation: Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar, Rohan Padhye, Miryung Kim. (2020). "BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction." ASE 2020. 1(2). https://conf.researchr.org/details/ase-2020/ase-2020-papers/86/BigFuzz-Efficient-Fuzz-Testing-for-Data-Analytics-using-Framework-Abstraction