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34th Great Lakes Symposium on VLSI (GLSVLSI)

作者： Saravanan, Raghul Dinakarrao, Sai Manoj Pudukotai George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9798400706059

The increasing complexity and integration of diverse components in modern System-on-Chip (SoC) designs make them susceptible to a range of attacks. Unfortunately, a substantial disjunction persists between the sophisticated architectures of the SoCs and Design Verification (DV) techniques to detect such vulnerabilities. Recently, Hardware fuzzing, inspired by software testing, has been gaining attention for its efficient bug-detection capabilities in SoC designs. coverage metrics serve as a pivotal tool in assessing the efficacy of fuzzing techniques by gauging the extent to which the Design Under Test (DUT) design space is explored during the verification process. This paper endeavors to delve into various hardware coverage metrics, encompassing branch, statement, Finite State Machine (FSM), line, and expression coverage, in order to elucidate both the merits and demerits of existing hardware fuzzing methodologies. Furthermore, it seeks to explore how these coverage metrics can be harnessed to bolster the efficacy of hardware fuzzing, thereby augmenting bug detection rates and streamlining testing endeavors. This work provides an analysis on different coverage metrics that could be utilized and the impact of it on the overall design coverage for various IP blocks and CPU designs.

关键词： Hardware verification Hardware fuzzing coverage metrics Hardware vulnerabilities

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Test coverage metrics for the Network 21

Test Coverage Metrics for the Network

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ACM SIGCOMM Conference (SIGCOMM)

作者： Xu, Xieyang Beckett, Ryan Jayaraman, Karthick Mahajan, Ratul Walker, David Univ Washington Seattle WA 98195 USA Microsoft Albuquerque NM USA Intentionet Seattle WA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781450383837

Testing and verification have emerged as key tools in the battle to improve the reliability of networks and the services they provide. However, the success of even the best technology of this sort is limited by how effectively it is applied, and in today's enormously complex industrial networks, it is surprisingly easy to overlook particular interfaces, routes, or flows when creating a test suite. Moreover, network engineers, unlike their software counterparts, have no help to battle this problem-there are no metrics or systems to compute the quality of their test suites or the extent to which their networks have been verified. To address this gap, we develop a general framework to define and compute network coverage for stateless network data planes. It computes coverage for a range of network components (e.g., interfaces, devices, paths) and supports many types of tests (e.g., concrete versus symbolic;local versus end-to-end;tests that check network state versus those that analyze behavior). Our framework is based on the observation that any network dataplane component can be decomposed into forwarding rules and all types of tests ultimately exercise these rules using one or more packets. We build a system called Yardstick based on this framework and deploy it in Microsoft Azure. Within the first month of its deployment inside one of the production networks, it uncovered several testing gaps and helped improve testing by covering 89% more forwarding rules and 17% more network interfaces.

关键词： coverage metrics network verification reliability

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Requirements driven falsification with coverage metrics 13

Requirements driven falsification with coverage metrics

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International Conference on Embedded Software (EMSOFT)

作者： Dokhanchi, Adel Zutshi, Aditya Sriniva, Rahul T. Sankaranarayanan, Sriram Fainekos, Georgios Arizona State Univ Tempe AZ 85287 USA Univ Colorado Boulder CO USA

ISBN: (纸本)9781467380799

Specification guided falsification methods for hybrid systems have recently demonstrated their value in detecting design errors in models of safety critical systems. In specification guided falsification, the correctness problem, i.e., does the system satisfy the specification, is converted into an optimization problem where local negative minima indicate design errors. Due to the complexity of the resulting optimization problem, the problem is solved iteratively by performing a number of simulations on the system. Even though it is theoretically guaranteed that falsification methods will eventually find the bugs in the system, in practice, the performance of these methods, i.e., how many tests/simulations are executed before a bug is detected, depends on the specification, on the system and on the optimization method. In this paper, we de fine and utilize coverage metrics on the state space of hybrid systems in order to improve the performance of the falsification methods.

关键词： formal specification coverage metrics iterative number local negative minima optimization problem requirements driven falsification method Aerospace electronics Measurement Robustness Semantics Testing Trajectory Yttrium avionics Rare earths yttrium Semantics trajectory falsification design error bugs hybrid systems semiotics Formal specifications state space optimization method Metronidazole Robustness

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Verification of petri nets based simulation models using coverage metrics 15

Verification of petri nets based simulation models using cov...

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3rd ACM Conference on SIGSIM-Principles of Advanced Discrete Simulation, SIGSIM-PADS 2015

作者： Rabe, Markus Deininger, Maik Scheidler, Anne Antonia Department of IT in Production and Logistics TU Dortmund University Leonhard-Euler-Str. 5 Dortmund Germany

ISBN: (纸本)9781450335836

Through the wast growth of information technologies, verification is of high importance in many research fields. Most of the models describing modelling procedures include verification and validation as a single step or as an accompanying activity. Unfortunately, most of these models do not give more detailed operation guidelines. In this context, it is common that verification is necessary, but in real life projects there is often no time nor men power for verification, particularly, if verifying is an accompanying activity as in simulation projects. The complexity of simulation projects complicates a proper implementation of verification. Therefore, it is a common method to tailor the procedure models. In this paper we concentrate on modelling and introduce an approach for automated verification. Petri nets are widely used as modelling method. Petri nets are well defined and based on graphs. Thus, this paper introduces an approach for mapping petri-net-based models to control flow graphs. Then it is shown, how this mapping can be utilised for applying coverage metrics. This enables projects to use software testing methods of coverage for verifying petri-netbased simulation models. As a benefit, project time and personnel can be saved. © 2015 ACM.

关键词： software testing modelling coverage metrics verification petri nets graphs

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coverage-Guided Testing for Recurrent Neural Networks

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IEEE TRANSACTIONS ON RELIABILITY 2022年第3期71卷 1191-1206页

作者： Huang, Wei Sun, Youcheng Zhao, Xingyu Sharp, James Ruan, Wenjie Meng, Jie Huang, Xiaowei Univ Liverpool Liverpool L69 3BX Merseyside England Queens Univ Belfast Belfast BT7 1NN Antrim North Ireland Def Sci & Technol Lab Salisbury SP4 0JQ Wilts England Univ Exeter Exeter EX4 4PY Devon England Loughborough Univ Loughborough LE11 3TU Leics England

Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerability to input perturbation is also known. Aligning with a view from software defect detection, this article aims to develop a coverage-guided testing approach to systematically exploit the internal behavior of RNNs, with the expectation that such testing can detect defects with high possibility. Technically, the long short-term memory network (LSTM), a major class of RNNs, is thoroughly studied. A family of three test metrics are designed to quantify not only the values but also the temporal relations (including both stepwise and bounded-length) exhibited when LSTM processing inputs. A genetic algorithm is applied to efficiently generate test cases. The test metrics and test case generation algorithm are implemented into a tool testRNN, which is then evaluated on a set of LSTM benchmarks. Experiments confirm that testRNN has advantages over the state-of-the-art tool DeepStellar and attack-based defect detection methods, owing to its working with finer temporal semantics and the consideration of the naturalness of input perturbation. Furthermore, testRNN enables meaningful information to be collected and exhibited for users to understand the testing results, which is an important step toward interpretable neural network testing.

关键词： Measurement Testing Tools Semantics Recurrent neural networks Software Logic gates coverage-guided testing coverage metrics recurrent neural networks (RNNs) test case generation

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PhysCov: Physical Test coverage for Autonomous Vehicles 2023

PhysCov: Physical Test Coverage for Autonomous Vehicles

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32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Hildebrandt, Carl von Stein, Meriel Elbaum, Sebastian Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9798400702211

Adequately exercising the behaviors of autonomous vehicles is fundamental to their validation. However, quantifying an autonomous vehicle's testing adequacy is challenging as the system's behavior is influenced both by its state as well as its physical environment. To address this challenge, our work builds on two insights. First, data sensed by an autonomous vehicle provides a unique spatial signature of the physical environment inputs. Second, given the vehicle's current state, inputs residing outside the autonomous vehicle's physically reachable regions are less relevant to its behavior. Building on those insights, we introduce an abstraction that enables the computation of a physical environment-state coverage metric, PhysCov. The abstraction combines the sensor readings with a physical reachability analysis based on the vehicle's state and dynamics to determine the region of the environment that may affect the autonomous vehicle. It then characterizes that region through a parameterizable geometric approximation that can trade quality for cost. Tests with the same characterizations are deemed to have had similar internal states and exposed to similar environments and thus likely to exercise the same set of behaviors, while tests with distinct characterizations will increase PhysCov. A study on two simulated and one real system's dataset examines PhysCovs's ability to quantify an autonomous vehicle's test suite, showcases its characterization cost and precision, investigates its correlation with failures found and potential for test selection, and assesses its ability to distinguish among real-world scenarios.

关键词： Test Adequacy coverage metrics Autonomous Systems

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RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第10期48卷 4167-4180页

作者： Guo, Jianmin Zhang, Quan Zhao, Yue Shi, Heyuan Jiang, Yu Sun, Jiaguang Tsinghua Univ Minist Educ Sch Sofware Beijing 100084 Peoples R China Tsinghua Univ Minist Educ Beijing Natl Res Ctr Informat Sci & Technol Key Lab Informat Syst Secur Beijing 100084 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Guangdong Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China

While massive efforts have been investigated in adversarial testing of convolutional neural networks (CNN), testing for recurrent neural networks (RNN) is still limited and leaves threats for vast sequential application domains. In this paper, we propose an adversarial testing framework RNN-Test for RNN systems, focusing on sequence-to-sequence (seq2seq) tasks of widespread deployments, not only classification domains. First, we design a novel search methodology customized for RNN models by maximizing the inconsistency of RNN states against their inner dependencies to produce adversarial inputs. Next, we introduce two state-based coverage metrics according to the distinctive structure of RNNs to exercise more system behaviors. Finally, RNN-Test solves the joint optimization problem to maximize state inconsistency and state coverage, and crafts adversarial inputs for various tasks of different kinds of inputs. For evaluations, we apply RNN-Test on four RNN models of common structures. On the tested models, the RNN-Test approach is demonstrated to be competitive in generating adversarial inputs, outperforming FGSM-based and DLFuzz-based methods to reduce the model performance more sharply with 2.78% to 37.94% higher success (or generation) rate. RNN-Test could also achieve 52.65% to 66.45% higher adversary rate than testRNN on MNIST LSTM model, as well as 53.76% to 58.02% more perplexity with 16% higher generation rate than DeepStellar on PTB language *** with the traditional neuron coverage, the proposed state coverage metrics as guidance excel with 4.17% to 97.22% higher success (or generation) rate.

关键词： Testing Neurons Convolutional neural networks Measurement Task analysis Recurrent neural networks Perturbation methods Adversarial testing recurrent neural networks coverage metrics

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Important-unit coverage for Recurrent Neural Network 32

Important-unit Coverage for Recurrent Neural Network

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32nd IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Liu, Xu Li, Honghui Wang, Rui Jiang, Zhouxian Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665426039

Nowadays, many latest systems are typical cyber physical systems (CPS), such as self-driving systems, medical monitoring, industrial control systems and robotics systems. Some of these fields involve speech emotion recognition based on deep learning technology. Therefore, the safety issues brought by deep neural networks cannot be ignored. Recurrent neural network (RNN) is one of several mainstream directions in speech emotion recognition. However, limited research has been done on RNN testing. In this paper, we define important-unit coverage metric for a classic RNN architecture, long short-term memory network (LSTM), to guide the generation of test cases and measure the test adequacy. We implement our experiments on a speech emotion dataset named Emo-DB. We also compare our method with some existing test coverage metrics for RNN. Experimental results show that we have consistent performance comparing with these metrics and can generate more test cases than neuron coverage.

关键词： LSTM coverage metrics test case generation

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Automating Pattern Selection for Assurance Case Development for Cyber-Physical Systems 1

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41st International Conference on Safety, Reliability, and Security of Computer-Based Systems (SAFECOMP)

作者： Ramakrishna, Shreyas Jin, Hyunjee Dubey, Abhishek Ramamurthy, Arun Vanderbilt Univ Inst Software Integrated Syst 221 Kirkland Hall Nashville TN 37235 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9783031148354

ISBN: (纸本)9783031148354;9783031148347

Assurance Cases are increasingly being required for regulatory acceptance of Cyber-Physical Systems. However, the ever-increasing complexity of these systems has made the assurance cases development complex, labor-intensive and time-consuming. Assurance case fragments called patterns are used to handle the complexity. The state-of-the-art approach has been to manually select generic patterns from online catalogs, instantiate them with system-specific information, and assemble them into an assurance case. While there has been some work in automating the instantiation and assembly, a less researched area is the automation of the pattern selection process, which takes a considerable amount of the assurance case development time. To close this automation gap, we have developed an automated pattern selection workflow that handles the selection problem as a coverage problem, intending to find the smallest set of patterns that can cover the available system artifacts. For this, we utilize the ontology graphs of the system artifacts and the patterns and perform graph analytics. The selected patterns are fed into an external instantiation function to develop an assurance case. Then, they are evaluated for coverage using two coverage metrics. An illustrative autonomous vehicle example is provided, demonstrating the utility of the proposed workflow in developing an assurance case with reduced efforts and time compared to the manual development alternative.

关键词： Cyber physical systems Assurance case Patterns GSN Optimization Ontology Graph isomorphism coverage metrics

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A taxonomy of metrics for GUI-based testing research: A systematic literature review

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INFORMATION AND SOFTWARE TECHNOLOGY 2022年第0期152卷

作者： Coppola, Riccardo Alegroth, Emil Politecn Torino Dept Control & Comp Engn Turin Italy Blekinge Inst Technol Karlskrona Sweden

Context: GUI-based testing is a sub-field of software testing research that has emerged in the last three decades. GUI-based testing techniques focus on verifying the functional conformance of the system under test (SUT) through its graphical user interface. However, despite the research domains growth, studies in the field have low reproducibility and comparability. One observed cause of these phenomena is identified as a lack of research rigor and commonly used metrics, including coverage ***: We aim to identify the most commonly used metrics in the field and formulate a taxonomy of coverage metrics for GUI-based testing ***: We adopt an evidence-based approach to build the taxonomy through a systematic literature review of studies in the GUI-based testing domain. Identified papers are then analyzed with Open and Axial Coding techniques to identify hierarchical and mutually exclusive categories of metrics with common characteristics, usages, and ***: Through the analysis of 169 papers and 315 metric definitions, we obtained a taxonomy with 55 codes (common names for metrics), 17 metric categories, and 4 higher level categories: Functional Level, GUI Level, Model Level and Code Level. We measure a higher number of mentions of Model and Code level metrics over Functional and GUI level ***: We propose a taxonomy for use in future GUI-based testing research to improve the general quality of studies in the domain. In addition, the taxonomy is perceived to help enable more replication studies as well as macro-analysis of the current body of research.

关键词： Software testing GUI -based testing coverage metrics Taxonomies Software verification and validation

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