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Equivalent Version Sets Testing Method for Android Applications based on Code Analysis

International Journal of Performability engineering 2019年第7期15卷 2008-2018页

作者： Huang, Song Yang, Sen Yao, Yongming Chen, Lele Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

The Android system is an open source mobile operating system that has been released in numerous versions. Android fragmentation is becoming more and more serious. This paper shows how different Android runtime environments affect test coverage results. To address this limitation, we run apps on all Android versions to collect coverage rate and also present an algorithm to generate equivalent test runtime-environment-set to exercise mobile apps. Our approach is to systematically test the targeted code of Android apps based on code analysis. It analyzes the decompiled code that identifies the code related to the Android SDK version and then generates the corresponding test cases. An empirical study of the practical usefulness of the technique is presented for six widely-used industrial apps. The test result shows that our equivalence test runtime-environment-set only requires less than half of all versions, which dramatically reduces the test resources. Moreover, the method coverage of these applications increased by an average of 49.3% on all versions and 46.8% on equivalent test runtime-environment-set, respectively. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Testing

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Test set augmentation technique for deep learning image classifiers

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International Journal of Performability engineering 2019年第7期15卷 1998-2007页

作者： Chen, Qiang Hui, Zhanwei Liu, Jialuo Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

Widely applied in various fields, deep learning (DL) is becoming the key driving force in the industry. Although it has achieved great success in artificial intelligence tasks, similar to traditional software, it has defects involving unpredictable accidents and losses due to failure. To ensure the quality of DL software, adequate testing needs to be carried out. In this paper, we propose a test set augmentation technique based on an adversarial example generation algorithm for image classification deep neural networks (DNNs). It can generate a large number of useful test cases, especially when test cases are insufficient. We briefly introduce the adversarial example generation algorithm and implement the framework of our method. We conduct experiments on classic DNN models and datasets. We further evaluate the test set by using a coverage metric based on states of the DNN. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Deep neural networks

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Bug report classification based on vector space model

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International Journal of Performability engineering 2019年第8期15卷 2071-2080页

作者： Chen, Lele Huang, Song Sun, Jinlei Hui, Zhanwei Yang, Sen Command and Control Engineering College Army Engineering University PLA Nanjing210007 China

As a vehicle for recording and tracking defects, bug reports provide a basis for solving software quality problems. Currently, software testing is often carried out in a multi-person and parallel state. The integration process of numerous bug reports, such as moving fake or duplication bug reports, is facing severe challenges. Therefore, this paper proposes an automatic detection modus for bug reports based on the vector space model. After pre-processing the bug report, a matching library is created according to the test requirements and test report samples. The vector space model is used to calculate the similarity between the two, and the correctness of the bug report is detected based on this. Experiments with the data of a software test contest show that the modus proposed in this paper can correctly judge most bug reports, effectively improving the efficiency of de-false and de-duplication. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Software testing

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High-Order Discontinuous Galerkin Schemes for Radiation Hydrodynamics Equations in the Equilibrium-Diffusion Limit

SSRN

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SSRN 2023年

作者： Peng, Gang Luo, Dongmi Qiu, Jianxian Chen, Yibing Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China Institute of Applied Physics and Computational Mathematics National Key Laboratory of Computational Physics Beijing100088 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China

In this paper, the high-order discontinuous Galerkin (DG) schemes are presented for radiation hydrodynamics equations in the equilibrium-diffusion limit. The governor equations contain two parts, one part is a hyperbolic system of conservation laws and another part is a radiative heat transfer term. The discrete schemes are constructed in three parts and possess characteristics of the high-order, oscillation-free and efficient. Firstly, the DG method and local DG (LDG) method are respectively used to discretize the hyperbolic system and radiative heat transfer term to achieve high-order spatial accuracy. Secondly, the combination of the KXRCF shock detector, Hermite WENO limiter, and positivity-preserving limiter is utilized to enhance the robustness of the discretization schemes. Finally, the implicit-explicit schemes are employed for time discretization to enable the schemes suitable for larger time steps. Numerical experiments have verified that the new schemes are high-order accuracy, oscillation-free and efficiency. © 2023, The Authors. All rights reserved.

关键词： Hydrodynamics

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A Distributed Approach for User Association and UAV Deployment in QoE-aware Multi-UAV Networks

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IEEE Transactions on Network and Service Management 2025年

作者： He, Yuan Liu, Yaqun Xie, Jun Army Engineering University of PLA College of Command and Control Engineering Jiangsu Nanjing210007 China PLA Army Academy of Artillery and Air Defense Anhui Hefei230031 China

Using unmanned aerial vehicles (UAVs) as aerial base stations (BSs) has gained increasing attention recently. In this paper, we investigate the problems of ground user (GU) association and UAV-BS deployment in a multi-UAV assisted wireless network using a distributed approach, aiming to maximize the total quality of experience (QoE) of GUs. Since the two problems are coupled to each other, we adopt an alternating optimization approach to optimize the GU association and UAV-BS deployment alternately. We model the GU association problem as a coalition formation game (CFG), and a distributed algorithm based on switch rule and swap rule is proposed to solve the stable coalition partition of the GU association CFG. Besides, we propose a distributed algorithm based on particle swarm optimization (PSO) algorithm to solve the deployment position of each UAV-BS. The proposed algorithms can run online on UAV-BSs in a distributed manner. We evaluate the performance of the proposed algorithms through simulation experiments. Simulation results demonstrate that the proposed algorithms can achieve a high total QoE of GUs in limited number of iterations. © 2004-2012 IEEE.

关键词： Particle swarm optimization (PSO)

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DeepBoundary: A Coverage Testing Method of Deep Learning Software based on Decision Boundary Representation

DeepBoundary: A Coverage Testing Method of Deep Learning Sof...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Yue Liu Lichao Feng Xingya Wang Shiyu Zhang College of Computer Science and Technology Nanjing Tech University Nanjing Jiangsu China Institute of Command and Control Engineering Army Engineering University of PLA Nanjing Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China

With the increasing application of Deep Learning (DL) Software in safety-critical fields such as autonomous driving, we need adequate testing to ensure software quality. Observing the decision-making behavior of a Deep Neural Network (DNN) is an essential step in DL software testing. Taking Guiding Deep Learning System Testing Using Surprise Adequacy (SADL) as an example, it uses the independent neuron activation values in the DNN to represent the decision-making behavior. However, the behavior of the DNN needs to be jointly determined by the continuous outputs of all neurons. As a result, the coverage value of SADL constant volatility and lack of stability. To mitigate this problem, we propose a coverage testing method based on the decision boundary representation, DeepBoundary, for the decision-making behavior of DL software. Unlike SADL, DeepBoundary generates decision boundary data to represent the decision behavior of the DNN, which makes the testing results more stable. On this basis, we calculate the kernel density between the testing data and the decision boundary data. It measures the position of the testing data in the decision space and the distance from the decision boundary. Finally, as an adequacy indicator, we calculate the decision boundary density coverage (DBC) of the entire testing set. The experiment on the dataset MNIST and two DL software shows that DeepBoundary can generate actual decision boundary data. The average confidence error in the DNNs output layer is only 4.20E-05. Compared with SADL, DeepBoundary has a stronger correlation with the defect detection ratio, which can more accurately represent testing adequacy.

关键词： Deep learning Software testing System testing Correlation Decision making Neurons Software quality

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Research on ldos attack detection and defense mechanism in software defined networks 6th

Research on ldos attack detection and defense mechanism in s...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Xie, Shengxu Xing, Changyou Zhang, Guomin Wei, Xianglin Hu, Guyu Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China The 63rd Research Institute National University of Defense Technology Nanjing210007 China

ISBN: (纸本)9789811590306

The LDoS (Low rate Denial of Service) attack that aims at exhausting the limited SDN switch buffer resource is hard to detect and degrade network performance seriously. To solve such a problem, this paper proposes an SDN LDoS detection and defense mechanism ADAR (Attack-flow Detection and Attack-port Recognition), which can detect the attack flows based on the collected statistical data, and identify and suppress these attack flows. The experimental results show that ADAR can effectively detect the SDN switch buffer overflow LDoS attacks, and mitigate their impact by using the attack port suppression method. Meanwhile, it can also effectively alleviate the problem of switch buffer overflow caused by the normal traffic burst in the network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Software defined networking

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Detection of Backdoor Attacks Using Targeted Universal Adversarial Perturbations for Deep Neural Networks

SSRN

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SSRN 2023年

作者： Qu, Yubin Huang, Song Chen, Xiang Wang, Xingya College of Command and Control Engineering Army Engineering University of PLA Nanjing210007 China School of Information Engineering Jiangsu College of Engineering and Technology Nantong226001 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China School of Information Science and Technology Nantong University Nantong226019 China College of Computer Science and Technology Nanjing Tech University China

The backdoors using targeted universal adversarial perturbations against deep neurall networks has been explored. This backdoor does not require data poisoning or model tampering. Rretraining deep neural network models using adversarial training for security is time-consuming and does not apply to deep neural networks in runtime. We want to detect backdoors using a black-box testing approach. We observe that after superimposing random noise on the input of a backdoor attack, the output still tends to remain the same, so we propose Sequential Analysis method based on the Metamorphosis Testing (SAMT). We designed two metamorphic relations for test case generation. Using sequential sampling, we calculate the label stability rate and infer whether the image to be verified contains a backdoor based on the sequential probability ratio change. The experimental results show that our method has a higher backdoor detection success rate than the state-of-the-art universal adversarial perturbations detection algorithms. Moreover, our method does not need to use the model structure of deep neural networks, which has more adaptability and generalization ability. Based on our proposed method, we can simply add a backdoor detection layer to detect backdoors as early as possible, which can eventually alleviate the harm of such backdoors. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Statistical distribution of HI 21cm intervening absorbers as potential cosmic acceleration probes

arXiv

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arXiv 2022年

作者： Lu, Chang-Zhi Zhang, Tingting Zhang, Tong-Jie Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China College of Command and Control Engineering PLA Army Engineering University Nanjing210017 China Institute for Astronomical Science Dezhou University Dezhou253023 China

Damped Lyman-α Absorber (DLA), or HI 21cm Absorber (H21A), is an important probe to model-independently measure the acceleration of spectroscopic velocity (vS) via the Sandage-Loeb (SL) effect. Confined by the shortage of DLAs and Background Radio Sources (BRSs) with adequate information, the detectable amount of DLAs is ambiguous in the bulk of previous work. After differing the acceleration of scale factor (ä) from the first order time derivative of spectroscopic velocity (v¤S), we make a statistical investigation of the amount of potential DLAs in the most of this paper. Using Kernel Density Estimation (KDE) to depict general redshift distributions of BRSs, observed DLAs and a DLA detection rate with different limitations (1.4GHz flux, HI column density and spin temperature), we provide fitted multi-Gaussian expressions of the three components and their 1σ regions by bootstrap, with a proportional constant of H21As in detected DLAs, leading to the measurable number predictions of H21As for FAST, ASKAP and SKA1-Mid in HI absorption blind survey. In our most optimistic condition (F1.4GHz>10mJy, NHI > 2 × 1020cm−2 and TS>500K), the FAST, AKSAP and SKA1-Mid would probe about 80, 500 and 600 H21As respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Cosmology

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Deepkernel: 2d-Kernels Clustering Based Mutant Reduction for Cost-Effective Deep Learning Software Testing

SSRN

引用

SSRN 2023年

作者： Zhang, Shiyu Wang, Xingya Feng, Lichao Huang, Song Chen, Zhenyu Zhao, Zhihong Nanjing Tech University Jiangsu Nanjing211816 China College of Command and Control Engineering Army Engineering University of PLA Jiangsu Nanjing210001 China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210092 China College of Computing Science and Artificial Intelligence Suzhou City University Jiangsu Suzhou215104 China

Mutation testing has been demonstrated as a practical approach for evaluating the quality of deep learning (DL) testing datasets. However, the enormous mutants during testing lead to significant testing overhead. Feature clustering is a conventional method that reduces the number of mutants while preserving the mutants' distribution diversity. This distribution diversity is considered crucial for maintaining the effectiveness of testing assessment ability. DL software relies on convolutional kernels to extract data features and construct logic. Thus, using kernels to measure the differences among DL mutants is a feasible approach. This paper proposes DeepKernel, a convolutional kernel features clustering based reduction method. Specifically, it considers 2D-Kernel sparsity and 2D-Kernel entropy as kernel features. The features are clustered to construct a subset with equivalent testing assessment capability to the original set. Empirical studies on three classical DL models demonstrate that: (1) there is a significant correlation between the distribution diversity of the mutants and their testing assessment ability, as indicated by a Spearman Correlation Coefficient of 0.9689. (2) the reduced set maintains a similar distribution diversity and testing effectiveness as the original set. (3) when preserving the effectiveness of the mutation testing, our method reduces 63.47% of mutants and outperforms random selection. © 2023, The Authors. All rights reserved.

关键词： Software testing

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