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Can test input selection methods for deep neural network guarantee test diversity? A large-scale empirical study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.infsof.2017.08.004openalex-snapshot
MULTI: Multi-objective effort-aware just-in-time software defect prediction
inferredcites →https://doi.org/10.1016/j.infsof.2019.07.003openalex-snapshot
Improving defect prediction with deep forest
inferredcites →https://doi.org/10.1016/j.jss.2019.03.012openalex-snapshot
An empirical study on pareto based multi-objective feature selection for software defect prediction
inferredcites →https://doi.org/10.1016/j.jss.2020.110659openalex-snapshot
The impact factors on the performance of machine learning-based vulnerability detection: A comparative study
inferredcites →https://doi.org/10.1016/j.jss.2021.111036openalex-snapshot
SeCNN: A semantic CNN parser for code comment generation
inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning
inferredcites →https://doi.org/10.1109/access.2019.2961129openalex-snapshot
DeepCPDP: Deep Learning Based Cross-Project Defect Prediction
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr14067.2009openalex-snapshot
2009 IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/eurosp51992.2021.00002openalex-snapshot
2021 IEEE European Symposium on Security and Privacy
inferredcites →https://doi.org/10.1109/icse40121.2019openalex-snapshot
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
inferredcites →https://doi.org/10.1109/issre17520.2010openalex-snapshot
2010 IEEE 21st International Symposium on Software Reliability Engineering
inferredcites →https://doi.org/10.1109/mci.2018.2840738openalex-snapshot
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
inferredcites →https://doi.org/10.1109/sp33448.2015openalex-snapshot
2015 IEEE Symposium on Security and Privacy
inferredcites →https://doi.org/10.1109/tr.2019.2924932openalex-snapshot
Large-Scale Empirical Studies on Effort-Aware Security Vulnerability Prediction Methods
inferredcites →https://doi.org/10.1109/tse.2019.2940439openalex-snapshot
SEthesaurus: WordNet in Software Engineering
inferredcites →https://doi.org/10.1109/tse.2019.2962027openalex-snapshot
Machine Learning Testing: Survey, Landscapes and Horizons
inferredcites →https://doi.org/10.1145/152388.152391openalex-snapshot
A methodology for controlling the size of a test suite
inferredcites →https://doi.org/10.1145/267580.267590openalex-snapshot
Software unit test coverage and adequacy
inferredcites →https://doi.org/10.1145/3238147.3238172openalex-snapshot
Concolic testing for deep neural networks
inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot
DeepGauge: multi-granularity testing criteria for deep learning systems
inferredcites →https://doi.org/10.1145/3293882.3330579openalex-snapshot
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
inferredcites →https://doi.org/10.1145/3338906.3338930openalex-snapshot
Boosting operational DNN testing efficiency through conditioning
inferredcites →https://doi.org/10.1145/3338906.3338954openalex-snapshot
DeepStellar: model-based quantitative analysis of stateful deep learning systems
inferredcites →https://doi.org/10.1145/3368089.3409750openalex-snapshot
DeepSearch: a simple and effective blackbox attack for deep neural networks
inferredcites →https://doi.org/10.1145/3368089.3409754openalex-snapshot
Is neuron coverage a meaningful measure for testing deep neural networks?
inferredcites →https://doi.org/10.1145/3394112openalex-snapshot
Practical Accuracy Estimation for Efficient Deep Neural Network Testing
inferredcites →https://doi.org/10.1145/3395363.3397357openalex-snapshot
DeepGini: prioritizing massive tests to enhance the robustness of deep neural networks
inferredcites →https://doi.org/10.1145/3505243openalex-snapshot
A Survey on Deep Learning for Software Engineering
inferredcites →https://doi.org/10.1155/2018/7068349openalex-snapshot
Deep Learning for Computer Vision: A Brief Review
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/spe.3256openalex-snapshot
A lightweight adaptive random testing method for deep learning systems
inferred← citeshttps://doi.org/10.1002/stvr.1914openalex-snapshot
A Systematic Mapping Study of the Metrics, Uses and Subjects of Diversity‐Based Testing Techniques
inferred← citeshttps://doi.org/10.1007/s10661-025-14230-zopenalex-snapshot
Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm
inferred← citeshttps://doi.org/10.1016/j.infsof.2023.107331openalex-snapshot
Stratified random sampling for neural network test input selection
inferred← citeshttps://doi.org/10.1016/j.iswa.2022.200172openalex-snapshot
Assessing Operational Accuracy of CNN-Based Image Classifiers Using an Oracle Surrogate
inferred← citeshttps://doi.org/10.1109/ddecs63720.2025.11006797openalex-snapshot
DEAR-CNN: Data-Efficient Assessment of Resiliency in Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/qrs-c63300.2024.00041openalex-snapshot
DeepDTS: Diverse Test Selection Method for Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/tcad.2025.3610062openalex-snapshot
OpRA: Optimizing Resiliency Assessment for Deep Neural Networks
inferred← citeshttps://doi.org/10.1145/3643678openalex-snapshot
Test Optimization in DNN Testing: A Survey
inferred← citeshttps://doi.org/10.1145/3644388openalex-snapshot
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
inferred← citeshttps://doi.org/10.1145/3808229openalex-snapshot
TrajectoryTest: A Trajectory-Specific Test Input Prioritization Technique and Empirical Evaluation
inferred← citeshttps://doi.org/10.2298/csis230907045jopenalex-snapshot
Semantic feature-based test selection for deep neural networks: A frequency domain perspective
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