DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

Xie, Xiaofei; Ма, Лей; Juefei-Xu, Felix; Xue, Minhui; Chen, Hongxu; Liu, Yang; Zhao, Jianjun; Li, Bo; Yin, Jianxiong; See, Simon

doi:10.1145/3293882.3330579

Cited by 313 publications

(216 citation statements)

References 23 publications

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“…The results of accuracy in above two sections are based on the original testing data. To further investigate the quality of migrated/quantized models, we combine the existing tools TENSORFUZZ [46] and DEEPHUNTER [67] as data generator. We generate a largescale testing data by using MNIST and CIFAR-10 as inputs to capture the differential behaviors between the PC model and the migrated/quantized model.…”

Section: ) Migration and Quantization On Generated Datamentioning

confidence: 99%

“…DeepXplore [50] and DeepGauge [42] proposed the new testing criteria for deep learning testing. DeepTest [64], DeepHunter [67] and TensorFuzz [46] proposed coverageguided testing techniques, which mainly focus on feedforward neural networks. DeepStellar [26] is proposed to perform the quantitative analysis for recurrent neural networks (RNN).…”

Section: Deep Learning Testingmentioning

confidence: 99%

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An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

Guo

Chen

Xie

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Self Cite

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Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice.To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks and platforms by using two types of DNN architectures and three popular datasets. (1) For development process, we investigate the prediction accuracy under the same runtime training configuration or same model weights/biases. We also study the adversarial robustness of trained models by leveraging the existing adversarial attack techniques. The experimental results show that the computing differences across frameworks could result in an obvious prediction accuracy decline, which should draw the attention of DL developers. (2) For deployment process, we investigate the prediction accuracy and performance (refers to time cost and memory consumption) when the trained models are migrated/quantized from PC to real mobile devices and web browsers. The DL platform study unveils that the migration and quantization still suffer from compatibility and reliability issues. Meanwhile, we find several DL software bugs by using the results as a benchmark. We further validate the results through bug confirmation from stakeholders and industrial positive feedback to highlight the implications of our study. Through our study, we summarize practical guidelines, identify challenges and pinpoint new research directions, such as understanding the characteristics of DL frameworks and platforms, avoiding compatibility and reliability issues, detecting DL software bugs, and reducing time cost and memory consumption towards developing and deploying high quality DL systems effectively.

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Section: ) Migration and Quantization On Generated Datamentioning

confidence: 99%

Section: Deep Learning Testingmentioning

confidence: 99%

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

Guo

Chen

Xie

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Self Cite

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“…They often exploit them to drive test input generation. Since classical adequacy criteria based on the code's control flow graph are ineffective with NNs, as typically 100% control flow coverage of the code of an NN can be easily reached with few inputs, researchers have defined novel test adequacy criteria specifically targeted to neural networks (Kim et al 2019;Ma et al 2018bMa et al , 2019Sekhon and Fleming 2019;Sun et al 2018a, b;Pei et al 2017;Shen et al 2018;Guo et al 2018;Xie et al 2019).…”

Section: Addressed Problem (Rq 11)mentioning

confidence: 99%

“…Five works (7%) manipulate only the input data, i.e., they perform input level testing (Bolte et al 2019;Byun et al 2019;Henriksson et al 2019;Wolschke et al 2018). The majority of the papers (64%) operate at the ML model level (model level testing) (Cheng et al 2018a;Ding et al 2017;Du et al 2019;Dwarakanath et al 2018;Eniser et al 2019;Gopinath et al 2018;Groce et al 2014;Guo et al 2018;Kim et al 2019;Li et al 2018;Ma et al 2018bMa et al , c, d, 2019Murphy et al 2007aMurphy et al , b, 2008Murphy et al , b, 2009Nakajima and Bui 2016, 2019Odena et al 2019;Patel et al 2018;Pei et al 2017;Qin et al 2018;Saha and Kanewala 2019;Sekhon and Fleming 2019;Shen et al 2018;Shi et al 2019;Spieker and Gotlieb 2019;Strickland et al 2018;Sun et al 2018a, b;Tian et al 2018;Udeshi and Chattopadhyay 2019;Udeshi et al 2018;Uesato et al 2019;Xie et al 2018Xie et al , 2019Xie et al , 2011Zhang et al 2018aZhang et al , b, 2019Zhao a...…”

Section: Cost Of Testingmentioning

confidence: 99%

“…Nearly half of the analysed papers (56%) propose and evaluate a technique which is domain-agnostic, Fig. 7 Testing Levels i.e., in principle it may be applicable to any MLS (Aniculaesei et al 2018;Byun et al 2019;Cheng et al 2018a, b;Du et al 2019;Eniser et al 2019;Guo et al 2018;Henriksson et al 2019;Kim et al 2019;Li et al 2018;Ma et al 2018bMa et al , c, d, 2019Murphy et al 2007aMurphy et al , b, 2008Murphy et al , b, 2009Nakajima and Bui 2016, 2019Odena et al 2019;Pei et al 2017;Saha and Kanewala 2019;Sekhon and Fleming 2019;Shen et al 2018;Shi et al 2019;Sun et al 2018a, b;Tian et al 2018;Udeshi and Chattopadhyay 2019;Uesato et al 2019;Xie et al 2018Xie et al , 2019Xie et al , 2011Zhang et al 2018aZhang et al , 2019Zhao and Gao 2018). Around 30% proposed approaches are designed for autonomous systems (Abeysirigoonawardena et al 2019;Beglerovic et al 2017;Bühler and Wegener 2004;Klueck et al 2018;Li et al 2016;Mullins et al 2018;de Oliveira Neves et al 2016;Patel et al 2018;Strickland et al 2018;…”

Section: Domains (Rq 13)mentioning

confidence: 99%

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Testing machine learning based systems: a systematic mapping

et al. 2020

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Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. Objective: To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. Method: We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. Results: We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. Conclusions: The most active research areas in MLS testing address automated scenario/input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks.

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Ex ² : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks

Wang

Zhao

et al. 2022

Int J of Intelligent Sys

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Fuzzing is considered to be an essential approach to guarantee the reliability of deep neural networks (DNNs) based systems. The DNN fuzzing leverages various inputs prioritization methods to guide the testing process. The current research mainly focus on constructing testing metrics that symbolize the logical representation of the DNN to guide the generation of test cases, which neglects the potential performance brought by implementing heuristic algorithm. Moreover, the straightforward implementation of queue structure can not represent the metamorphic relationships between generated inputs in DNN fuzzing. Therefore, developing the appropriate heuristic algorithm‐based inputs prioritization method is critical to improve the performance of DNN fuzzers. In this paper, we propose a Monte Carlo Tree Search (MCTS) based inputs prioritization method called E x 2 $E{x}^{2}$ (Exploration and Exploitation) that formulates DNN testing exploration as the sequential decision process. The technique introduces an innovative tree‐structure design that schedules inputs from the statistical perspective. Different from traditional DNN testing, the batch pool is maintained in the form of nodes in MCTS. The links between nodes precisely represent the metamorphic relationship between input batches, which indicates the potential value for in‐depth search. Furthermore, a novel simulation mechanism is implemented to adapt MCTS in DNN testing, which attain better coverage feedback. The effectiveness of our method is comprehensively investigated on six popular deep learning models from LeNet and VGG families. The comparison experiments are conducted between DeepHunter, TensorFuzz, and DeepSmartFuzzer to demonstrate efficacy on various testing metrics. The experimental results show that the E x 2 $E{x}^{2}$ significantly enhance the coverage gain of DNN fuzzing up to 30% against the best performance in comparison groups.

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DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

Cited by 313 publications

References 23 publications

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

Testing machine learning based systems: a systematic mapping

Ex ² : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks

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DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

Cited by 313 publications

References 23 publications

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

Testing machine learning based systems: a systematic mapping

Ex 2 : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks

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Ex ² : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks