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检索条件"主题词=combinatorial coverage"

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combinatorial coverage framework for machine learning in multi-domain operations 4

Combinatorial coverage framework for machine learning in mul...

引用

Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV

作者： Cody, Tyler Kauffman, Justin Krometis, Justin Sobien, Dan Freeman, Laura Virginia Tech Natl Secur Inst 900 N Glebe Rd Arlington VA 22203 USA

ISBN: (纸本)9781510651036;9781510651029

Multi-domain operations (MDO) are characterized by simultaneous and sequential operations;rapid and continuous integration;and surprise. Machine learning (ML) for MDO is no different. Translated into ML, MDO requires highly assured yet rapid data and model fusion. Assurance demands robustness, reliability, and explainability, while speed demands computational efficiency and sample efficiency. combinatorial interaction testing offers explainable and rigorous techniques to ML for fusing data and models with runtime guarantees. But such methods are underexplored in the literature. combinatorial coverage has been applied to neuron- and layer-levels of neural networks, but only recently to ML in general. There are also ongoing debates of efficacy in the literature, but these debates are scoped to explainable deep learning. This work presents a framework for using combinatorial coverage for multi-domain operations. We discuss how coverage metrics can incorporate multi-modal meta-data and mission context into fusion processes, how coverage is oriented towards identifying gaps in and between sets of data, and how coverage can identify cases where performance is expected to be difficult. We conclude that combinatorial coverage should be considered a core capability for supporting ML in MDO.

关键词： multi-domain operations machine learning combinatorial coverage combinatorial interaction testing domain adaptation model fusion data fusion

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Anticipating Spectrogram Classification Error With combinatorial coverage Metrics

Anticipating Spectrogram Classification Error With Combinato...

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IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)

作者： Cody, Tyler Freeman, Laura Virginia Tech Natl Secur Inst Arlington VA 24061 USA

ISBN: (纸本)9798350335675

Recently, combinatorial interaction testing (CIT) has been applied to machine learning. Recent results demonstrate that combinatorial coverage metrics can correlate with classification error. However, these methods have not been applied to the area of cognitive communications. In this paper, we show that combinatorial coverage metrics can be used to anticipate spectrogram classification error for convolutional neural networks (CNNs) for batch sizes of 100-500 spectrograms. We propose a method that combines deep autoencoders, metric learning, and k-means clustering to learn a low-dimensional, discrete representation of spectrograms for coverage analysis. Our results show strong correlations which suggest that combinatorial coverage metrics can be used to anticipate spectrogram classification error. This finding identifies coverage as a new class of solution methods for monitoring and testing cognitive radios.

关键词： combinatorial coverage combinatorial testing machine learning spectrogram classification

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Active Learning with combinatorial coverage 21

Active Learning with Combinatorial Coverage

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Katragadda, Sai Prathyush Cody, Tyler Beling, Peter Freeman, Laura Virginia Tech Grado Dept Ind & Syst Engn Blacksburg VA USA Virginia Tech Virginia Tech Natl Secur Inst Arlington VA USA

ISBN: (纸本)9781665462839

Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.

关键词： active learning combinatorial coverage combinatorial interaction testing

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The Effect of combinatorial coverage for Neurons on Fault Detection in Deep Neural Networks 21

The Effect of Combinatorial Coverage for Neurons on Fault De...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Wang, Ziyuan Guo, Jinwu Chen, Yanshan She, Feiyan Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665478366

Many test adequacy metrics for deep neural networks (DNNs) were proposed to measure the quality of the testing of deep learning system. The combinatorial coverage of neurons was proposed because it considers the influence of neurons in the same layer on neurons in the next layer. However, it is still inconclusive whether the combinatorial coverage of neurons is beneficial to the testing of DNNs. We conduct an empirical study on MNIST dataset to answer how the combinatorial coverage of neurons affects the fault detection in DNNs. The experimental results show a medium or strong correlation between the fixed-strength combinatorial coverage of neurons and the number of adversarial examples for DNNs. Such a results suggests that it is feasible to use the combinatorial coverage of neurons to guide the testing of DNNs.

关键词： Deep neural networks combinatorial coverage adversarial examples correlation

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Ordered t -way Combinations for Testing State -based Systems 16

Ordered t -way Combinations for Testing State -based Systems

引用

16th IEEE International Conference on Software Testing, Verification and Validation (ICST)

作者： Kuhn, D. Richard Raunak, M. S. Kacker, Raghu N. Natl Inst Stand & Technol Gaithersburg MD 20899 USA

ISBN: (纸本)9798350333350

Fault detection often depends on the specific order of inputs that establish states which eventually lead to a failure. However, beyond basic structural coverage metrics, it is often difficult to determine if the code has been exercised sufficiently to ensure confidence in its functions. Measures are needed to ensure that relevant combinations of input values have been tested with adequate diversity of ordering to ensure correct operation. combinatorial testing and combinatorial coverage measures have been applied to many types of applications but have some deficiencies for verifying and testing state-based systems where the response depends on both input values and the current system state. In such systems, internal states change as input values are processed. Examples include network protocols, which may be in listening, partial connection, full connection, disconnected, and other states depending on the values of packet fields and the order of packets received. This paper discusses definitions for ordered t-way combination arrays and proves results regarding the construction of adequate blocks of test inputs, including consecutive ordering of combinations and with interleaving allowed. The application of the results to verify and test state- based systems is also illustrated.

关键词： combinatorial coverage combinatorial testing software testing structural coverage test coverage

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Do Judge a Test by its Cover Combining combinatorial and Property-Based Testing 30th

Do Judge a Test by its Cover Combining Combinatorial and Pro...

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30th European Symposium on Programming (ESOP) Held as Part of the 24th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Goldstein, Harrison Hughes, John Lampropoulos, Leonidas Pierce, Benjamin C. Univ Penn Philadelphia PA 19104 USA Chalmers Univ Technol & Quviq AB S-41296 Gothenburg Sweden Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9783030720193;9783030720186

Property-based testing uses randomly generated inputs to validate high-level program specifications. It can be shockingly effective at finding bugs, but it often requires generating a very large number of inputs to do so. In this paper, we apply ideas from combinatorial testing, a powerful and widely studied testing methodology, to modify the distributions of our random generators so as to find bugs with fewer tests. The key concept is combinatorial coverage, which measures the degree to which a given set of tests exercises every possible choice of values for every small combination of input features. In its "classical" form, combinatorial coverage only applies to programs whose inputs have a very particular shape-essentially, a Cartesian product of finite sets. We generalize combinatorial coverage to the richer world of algebraic data types by formalizing a class of sparse test descriptions based on regular tree expressions. This new definition of coverage inspires a novel combinatorial thinning algorithm for improving the coverage of random test generators, requiring many fewer tests to catch bugs. We evaluate this algorithm on two case studies, a typed evaluator for System F terms and a Haskell compiler, showing significant improvements in both.

关键词： combinatorial testing combinatorial coverage QuickCheck Property-based testing Regular tree expressions Algebraic data types

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ATTL: An Automated Targeted Transfer Learning with Deep Neural Networks

ATTL: An Automated Targeted Transfer Learning with Deep Neur...

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IEEE Global Communications Conference (GLOBECOM)

作者： Ahamed, Sayyed Farid Aggarwal, Priyanka Shetty, Sachin Lanus, Erin Freeman, Laura J. Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Norfolk VA 23529 USA Virginia Tech Hume Ctr Blacksburg VA USA Virginia Tech Dept Stat Blacksburg VA USA

ISBN: (纸本)9781728181042

Success of machine learning algorithms hinges on access to labeled dataset. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL incorporates gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. In this paper, we propose an automated targeted transfer learning (ATTL) method to resolve the transferability between source and target with minimal data requirements. The ATTL method decides how much target data is essential for model training, along with selected source data, to obtain the skateholder's specified performance metrics. The ATTL framework optimizes the system to select minimal target data based on two approaches: combinatorial coverage and adaptive selection methodology, along with specific source data for fine-tuning given a pre-trained source model. We evaluated the ATTL method on the Kaggle's 'planes in satellite imagery' dataset and the results identified that acquiring a small number of intentionally well-chosen samples from the target environment can achieve model performance of 97% in comparison to the baseline transfer learning accuracy of 92%.

关键词： Transfer learning Deep neural networks Automated source selection Fine-tuning combinatorial coverage

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CAMETRICS: A Tool for Advanced combinatorial Analysis and Measurement of Test Sets 11

CAMETRICS: A Tool for Advanced Combinatorial Analysis and Me...

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11th IEEE International Conference on Software Testing, Verification and Validation (ICST)

作者： Leithner, Manuel Kleine, Kristoffer Simos, Dimitris E. SBA Res A-1040 Vienna Austria

ISBN: (纸本)9781538663523

combinatorial testing (CT) has established itself as an efficient and effective approach for generating test sets, guaranteeing that all interactions of parameters up to a given strength t are covered. While numerous publications in recent years have addressed the problem of generating Covering Arrays (CAs), the central mathematical construct used in CT, as well as measuring various aspects of these test sets, there is a distinct lack of usable and automated means to derive these measurements and thus determine the combinatorial quality of a test set and evaluate the behavior of the algorithm that produced it. We therefore introduce CAMETRICS, a tool to compute various combinatorial coverage measures of a given test set, distribution of parameter value combinations, i.e. how the different t-tuples are distributed, as well as distance metrics and coverage gain per test. Such combinatorial analysis of test sets is crucial also for practitioners to estimate the residual risk that remains after testing by revealing untested parameter interactions. The tool is available as a web-based application allowing users to easily visualize the combinatorial metrics computed for test sets. Our experimental evaluation and the feature set of CAMETRICS compares favorably to existing tools in the field.

关键词： combinatorial testing t-way testing covering arrays combinatorial coverage distance metrics WebAssembly

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Using combinatorial Approaches for Testing Mobile Applications

Using Combinatorial Approaches for Testing Mobile Applicatio...

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7th IEEE International Conference on Software Testing, Verification and Validation (ICST)

作者： Vilkomir, Sergiy Amstutz, Brandi E Carolina Univ Dept Comp Sci Greenville NC 27858 USA

ISBN: (纸本)9780769551944

Device-specific faults are very common for mobile software applications. To avoid such faults and guarantee the reliability and quality of mobile applications, sufficient testing is required on different mobile devices, which is expensive and time-consuming. This makes the task of the optimal selection of mobile devices for testing important and interesting from both practical and theoretical points of view. The suggested approach in this paper is based on combinatorial methods for coverage of each device characteristics. The initial results of the experimental investigation using comparisons with a random selection of devices are provided and show that the proposed approach is effective and promising.

关键词： testing mobile applications device-specific failures combinatorial coverage

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