MuNN: Mutation Analysis of Neural Networks

Shen, Weijun; Wan, Jun; Chen, Zhenyu

doi:10.1109/qrs-c.2018.00032

Cited by 56 publications

(45 citation statements)

References 11 publications

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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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Section: Addressed Problem (Rq 11)mentioning

confidence: 99%

Section: Cost Of Testingmentioning

confidence: 99%

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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“…Adversarial attack (EP_AE) Reveal the defects in DNN by executing adversarial examples Targeted, non-targeted, L p -norm attack [28,29] Mutation testing (EP_MUT) Evaluate the testing adequacy by mutating training data, training program and trained model MuNN [30], DeepMutation [31] Metamorphic testing (EP_MT) Determine the system correctness by checking whether the metamorphic relation is satisfied DeepTest [14] Test prioritization (EP_TP) Measure the correctness of classification by the purity of test data DeepGini [32] Formal Verification Technique (EP_FV) Satisfiability Solver (EP_SS) Transform the safety verification to satisfiability solver problem Safety verification [33] Non-linear problem (EP_NLP) Transform the safety verification to non-linear problem Piecewise linear network verification [34] Symbolic interval analysis (EP_SIA) Transform the safety verification by analyzing symbolic interval ReluVal [35] Reachability analysis (EP_RA) Transform the safety verification by analyzing the reachability problem DeepGo [36] Abstract interpretation (EP_AI) Transform the safety verification to abstract interpretation AI 2 [37], Symbolic propagation [38] Evaluation Dataset (EP_ED)…”

Section: Metamorphic Testing Based Strategy (Ep_mt)mentioning

confidence: 99%

“…MuNN. MuNN [30] proposes five kinds of mutation operators according to the structure of neural network, including deleting neurons in the input layer and hidden layers, changing the bias, weights, and activation functions. A mutant neural network is said to be killed once its output is distinct from the output of the original network.…”

mentioning

confidence: 99%

ExtendAIST: Exploring the Space of AI-in-the-Loop System Testing

Dong

Singa

2020

Applied Sciences

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The AI-in-the-loop system (AIS) has been widely used in various autonomous decision and control systems, such as computing vision, autonomous vehicle, and collision avoidance systems. AIS generates and updates control strategies through learning algorithms, which make the control behaviors non-deterministic and bring about the test oracle problem in AIS testing procedure. The traditional system mainly concerns about properties of safety, reliability, and real-time, while AIS concerns more about the correctness, robustness, and stiffness of system. To perform an AIS testing with the existing testing techniques according to the testing requirements, this paper presents an extendable framework of AI-in-the-loop system testing by exploring the key steps involved in the testing procedure, named ExtendAIST, which contributes to define the execution steps of ExtendAIST and design space of testing techniques. Furthermore, the ExtendAIST framework provides three concerns for AIS testing, which include: (a) the extension points; (b) sub-extension points; and (c) existing techniques commonly present in each point. Therefore, testers can obtain the testing strategy using existing techniques directly for corresponding testing requirements or extend more techniques based on these extension points.

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Deriving and evaluating a fault model for testing data science applications

Jilani

Sherin

Ijaz

et al. 2022

J Software Evolu Process

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Data science (DS) applications not only suffer from traditional software faults but may also suffer from data‐specific and model‐related faults. Fault models play an important role in evaluating and designing tests for testing DS applications. The existing fault models do not consider DS specific faults. In this study, we built a fault model DS applications. We investigate the faults by using diverse approaches: (i) a multi‐vocal literature survey of published literature, (ii) semi‐structured interviews of industry experts. The Multi‐vocal study allows us to synthesize the existing knowledge from researchers and practitioners. Qualitative data from semi‐structured interviews provide us with insights into the nature of faults encountered by practitioners. We combine the results of (i) and (ii) to derive a detailed fault model. The developed fault model is further validated through a quantitative survey of industry practitioners, and the respondents were asked to identify the faults from our proposed fault model that they have experienced and classify those faults based on their severity as perceived by practitioners and its frequency. The results show that practitioners consider prediction bias and model decay as the most severe faults while data sampling and splitting faults along with feature engineering faults are the most frequent.

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MuNN: Mutation Analysis of Neural Networks

Cited by 56 publications

References 11 publications

Testing machine learning based systems: a systematic mapping

Testing machine learning based systems: a systematic mapping

ExtendAIST: Exploring the Space of AI-in-the-Loop System Testing

Deriving and evaluating a fault model for testing data science applications

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