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Seed Selection for Testing Deep Neural Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/stvr.294openalex-snapshot
Search‐based software test data generation: a survey
inferredcites →https://doi.org/10.1002/stvr.354openalex-snapshot
Search‐based software test data generation for string data using program‐specific search operators
inferredcites →https://doi.org/10.1007/978-3-319-89960-2_22openalex-snapshot
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
inferredcites →https://doi.org/10.1016/j.jocs.2017.09.015openalex-snapshot
A novel non-dominated sorting algorithm for evolutionary multi-objective optimization
inferredcites →https://doi.org/10.1016/j.jss.2017.09.018openalex-snapshot
QuickFuzz testing for fun and profit
inferredcites →https://doi.org/10.1016/j.patcog.2020.107332openalex-snapshot
Understanding adversarial attacks on deep learning based medical image analysis systems
inferredcites →https://doi.org/10.1016/s0950-5849(01)00189-6openalex-snapshot
Search-based software engineering
inferredcites →https://doi.org/10.1016/s0950-5849(01)00190-2openalex-snapshot
Evolutionary test environment for automatic structural testing
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1038/nature24270openalex-snapshot
Mastering the game of Go without human knowledge
inferredcites →https://doi.org/10.1109/aitest.2019.00013openalex-snapshot
DeepPath: Path-Driven Testing Criteria for Deep Neural Networks
inferredcites →https://doi.org/10.1109/ase.2011.6100082openalex-snapshot
Automated web application testing using search based software engineering
inferredcites →https://doi.org/10.1109/ase51524.2021.9678764openalex-snapshot
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-snapshot
FaceNet: A unified embedding for face recognition and clustering
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00108openalex-snapshot
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
inferredcites →https://doi.org/10.1109/icse-companion.2019.00134openalex-snapshot
Structural Test Coverage Criteria for Deep Neural Networks
inferredcites →https://doi.org/10.1109/icse-nier.2019.00030openalex-snapshot
Towards Improved Testing For Deep Learning
inferredcites →https://doi.org/10.1109/icse.2019.00081openalex-snapshot
Superion: Grammar-Aware Greybox Fuzzing
inferredcites →https://doi.org/10.1109/icse.2019.00108openalex-snapshot
Guiding Deep Learning System Testing Using Surprise Adequacy
inferredcites →https://doi.org/10.1109/icse43902.2021.00046openalex-snapshot
Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis
inferredcites →https://doi.org/10.1109/icst.2012.92openalex-snapshot
The Seed is Strong: Seeding Strategies in Search-Based Software Testing
inferredcites →https://doi.org/10.1109/issre.2018.00021openalex-snapshot
DeepMutation: Mutation Testing of Deep Learning Systems
inferredcites →https://doi.org/10.1109/ivs.2011.5940562openalex-snapshot
Towards fully autonomous driving: Systems and algorithms
inferredcites →https://doi.org/10.1109/saner.2019.8668044openalex-snapshot
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1126/science.aaw4399openalex-snapshot
Adversarial attacks on medical machine learning
inferredcites →https://doi.org/10.1145/1013886.1007528openalex-snapshot
Evolutionary testing of classes
inferredcites →https://doi.org/10.1145/1868048.1868049openalex-snapshot
Reducing qualitative human oracle costs associated with automatically generated test data
inferredcites →https://doi.org/10.1145/3236024.3264835openalex-snapshot
DLFuzz: differential fuzzing testing of deep learning systems
inferredcites →https://doi.org/10.1145/3238147.3238172openalex-snapshot
Concolic testing for deep neural networks
inferredcites →https://doi.org/10.1145/3238147.3238192openalex-snapshot
Testing autonomous cars for feature interaction failures using many-objective search
inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot
DeepGauge: multi-granularity testing criteria for deep learning systems
inferredcites →https://doi.org/10.1145/3243734openalex-snapshot
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
inferredcites →https://doi.org/10.1145/3324884.3416621openalex-snapshot
Multiple-boundary clustering and prioritization to promote neural network retraining
inferredcites →https://doi.org/10.1145/3338906.3338954openalex-snapshot
DeepStellar: model-based quantitative analysis of stateful deep learning systems
inferredcites →https://doi.org/10.1145/3343031.3351088openalex-snapshot
Black-box Adversarial Attacks on Video Recognition Models
inferredcites →https://doi.org/10.1145/3368089.3409671openalex-snapshot
Correlations between deep neural network model coverage criteria and model quality
inferredcites →https://doi.org/10.1145/3368089.3409729openalex-snapshot
Fuzzing: on the exponential cost of vulnerability discovery
inferredcites →https://doi.org/10.1145/3368089.3409730openalex-snapshot
Model-based exploration of the frontier of behaviours for deep learning system testing
inferredcites →https://doi.org/10.1145/3368089.3409748openalex-snapshot
Boosting fuzzer efficiency: an information theoretic perspective
inferredcites →https://doi.org/10.1145/3377811.3380331openalex-snapshot
White-box fairness testing through adversarial sampling
inferredcites →https://doi.org/10.1145/3377811.3380368openalex-snapshot
Towards characterizing adversarial defects of deep learning software from the lens of uncertainty
inferredcites →https://doi.org/10.1145/3377811.3380415openalex-snapshot
Fuzz testing based data augmentation to improve robustness of deep neural networks
inferredcites →https://doi.org/10.1145/3395363.3397346openalex-snapshot
Effective white-box testing of deep neural networks with adaptive neuron-selection strategy
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/10.1145/3460319.3464795openalex-snapshot
Seed selection for successful fuzzing
inferredcites →https://doi.org/10.1145/3460319.3464801openalex-snapshot
Exposing previously undetectable faults in deep neural networks
inferredcites →https://doi.org/10.1145/3460319.3464811openalex-snapshot
DeepHyperion: exploring the feature space of deep learning-based systems through illumination search
inferredcites →https://doi.org/10.1145/3510003openalex-snapshot
Proceedings of the 44th International Conference on Software Engineering
inferredcites →https://doi.org/10.1145/3510003.3510232openalex-snapshot
Adaptive test selection for deep neural networks
inferredcites →https://doi.org/10.1145/3544792openalex-snapshot
Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems
inferredcites →https://doi.org/10.1145/3569935openalex-snapshot
Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems
inferredcites →https://doi.org/10.1145/3576040openalex-snapshot
Input Distribution Coverage: Measuring Feature Interaction Adequacy in Neural Network Testing
inferredcites →https://doi.org/10.14722/ndss.2019.23371openalex-snapshot
REDQUEEN: Fuzzing with Input-to-State Correspondence
inferredcites →https://doi.org/10.3934/jimo.2020009openalex-snapshot
Non-dominated sorting methods for multi-objective optimization: Review and numerical comparison
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1609.08976openalex-snapshot
Variational Autoencoder for Deep Learning of Images, Labels and Captions
inferredcites →https://doi.org/10.48550/arxiv.1709.09844openalex-snapshot
Distance-based Confidence Score for Neural Network Classifiers
inferredcites →https://doi.org/10.48550/arxiv.1801.02613openalex-snapshot
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
inferredcites →https://doi.org/10.48550/arxiv.1803.08533openalex-snapshot
Understanding Measures of Uncertainty for Adversarial Example Detection
inferredcites →https://doi.org/10.48550/arxiv.1809.01266openalex-snapshot
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
inferredcites →https://doi.org/10.48550/arxiv.1903.12261openalex-snapshot
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
inferredcites →https://doi.org/10.48550/arxiv.2002.05990openalex-snapshot
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
inferredcites →https://doi.org/10.5555/1953048.2078186openalex-snapshot
Natural Language Processing (Almost) from Scratch
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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/s00521-024-10794-yopenalex-snapshot
Metamorphic testing of deep neural network-based autonomous driving systems using behavioural domain adequacy
inferred← citeshttps://doi.org/10.1007/s42452-025-07284-8openalex-snapshot
Advanced techniques for seed quality assessment and germination monitoring
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107651openalex-snapshot
Improving seed quality with historical fuzzing results
inferred← citeshttps://doi.org/10.1016/j.infsof.2026.108248openalex-snapshot
Dynamic learning trajectory pruning and feature-enhanced test case prioritization for deep neural networks
inferred← citeshttps://doi.org/10.1016/j.jnca.2025.104212openalex-snapshot
DeFinder: Error-sensitive testing of deep neural networks via vulnerability interpretation
inferred← citeshttps://doi.org/10.1029/2025ea004937openalex-snapshot
Assessing Deep Learning Segmentation Models for Mapping Landslides From LiDAR‐Based Elevation Data
inferred← citeshttps://doi.org/10.1038/s41598-026-49010-3openalex-snapshot
Class-adaptive oracle-free metamorphic test case prioritization framework for vision-based deep neural networks
inferred← citeshttps://doi.org/10.1049/sfw2/8657455openalex-snapshot
ReinSeed: Reinforcement Fuzz Testing With Multiphase Seed Optimization for Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1109/access.2026.3721657openalex-snapshot
DGFuzz: Region-Level Defect Feedback for Directed Fuzz Testing of Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/apsec65559.2024.00011openalex-snapshot
A DNN Fuzz Testing Method Based on Gradient-Weighted Class Activation Map
inferred← citeshttps://doi.org/10.1109/tifs.2024.3402153openalex-snapshot
Exploiting the Adversarial Example Vulnerability of Transfer Learning of Source Code
inferred← citeshttps://doi.org/10.1145/3672457openalex-snapshot
GIST : Generated Inputs Sets Transferability in Deep Learning
inferred← citeshttps://doi.org/10.1145/3680464openalex-snapshot
Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
inferred← citeshttps://doi.org/10.1145/3688835openalex-snapshot
Neuron Semantic-Guided Test Generation for Deep Neural Networks Fuzzing
inferred← citeshttps://doi.org/10.1145/3744916.3773236openalex-snapshot
Training on Clean Data but Getting Backdoored Models! A Poisoning Attack on Code Encoders
inferred← citeshttps://doi.org/10.3390/ijgi15030105openalex-snapshot
SSKEM: A Global Pointer Network Model for Joint Entity and Relation Extraction in Storm Surge Texts
inferred← citeshttps://doi.org/10.7717/peerj-cs.4114openalex-snapshot
Systematic review of test case generation strategies for deep learning systems
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