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Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Confidence‐driven weighted retraining for predicting safety‐critical failures in autonomous driving systems
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A taxonomy of model‐based testing approaches
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Pearson’s Correlation Coefficient
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Fitness Functions for Testing Automated and Autonomous Driving Systems
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Does Diversity Improve the Test Suite Generation for Mobile Applications?
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On Parameter Tuning in Search Based Software Engineering
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Search Based Software Engineering: Techniques, Taxonomy, Tutorial
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Testing machine learning based systems: a systematic mapping
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A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
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A CLASS OF LOCAL INTERPOLATING SPLINES
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A Memetic Algorithm for whole test suite generation
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Wild patterns: Ten years after the rise of adversarial machine learning
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Introduction to Information Retrieval
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A brief introduction to weakly supervised learning
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Lecture Notes in Computer Science 1205
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Gradient-based learning applied to document recognition
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DeepMutation++: A Mutation Testing Framework for Deep Learning Systems
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DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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Selective pressure in evolutionary algorithms: a characterization of selection mechanisms
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Augmenting test suites effectiveness by increasing output diversity
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Guiding Deep Learning System Testing Using Surprise Adequacy
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Distribution-Aware Testing of Neural Networks Using Generative Models
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Using Exploration Focused Techniques to Augment Search-Based Software Testing: An Experimental Evaluation
inferredcites →https://doi.org/10.1109/icst.2016.33openalex-snapshot
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
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DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems
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SBST Tool Competition 2021
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Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tevc.2015.2428292openalex-snapshot
Memetic Viability Evolution for Constrained Optimization
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A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
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Testing advanced driver assistance systems using multi-objective search and neural networks
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Proceedings of the 21st international conference on Software engineering
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Testing vision-based control systems using learnable evolutionary algorithms
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DLFuzz: differential fuzzing testing of deep learning systems
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DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems
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Automatically testing self-driving cars with search-based procedural content generation
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DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
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Diversity-based web test generation
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Model-based exploration of the frontier of behaviours for deep learning system testing
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Proceedings of the 22nd international conference on Software engineering - ICSE '00
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Taxonomy of real faults in deep learning systems
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Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis
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Exposing previously undetectable faults in deep neural networks
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DeepHyperion: exploring the feature space of deep learning-based systems through illumination search
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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inferredcites →https://doi.org/10.48550/arxiv.2109.07514openalex-snapshot
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
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Search-based software test data generation: a survey
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Automated Feature Extraction for Testing Deep Learning Systems Through Illumination Search
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Fundamental Approaches to Software Engineering
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Feature-Guided Analysis of Neural Networks
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DevOps Testing for Cyber-Physical Systems
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Two is better than one: digital siblings to improve autonomous driving testing
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An industrial experience report on applying search-based boundary input generation to cyber-physical systems
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When uncertainty leads to unsafety: Empirical insights into the role of uncertainty in unmanned aerial vehicle safety
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Mosaic: model-based safety analysis for AI-enabled cyber physical system
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DeepRegion: Black-box efficient testing for DNNs based on region analysis
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Rough set theory applied to finite dimensional vector spaces
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Automatically Generating Content for Testing Autonomous Vehicles from User Descriptions
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When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study
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DeepNaqqal: Human-Aligned Automated Validation of Test Inputs for Deep Learning
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SBFT Tool Competition 2023 - Cyber-Physical Systems Track
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A Sequential Metamorphic Testing Framework for Understanding Autonomous Vehicle's Decisions
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Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI
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Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
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TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks
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Search-Based DNN Testing and Retraining With GAN-Enhanced Simulations
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LongTest: Test Prioritization for Long Text Files
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CIT4DNN: Generating Diverse and Rare Inputs for Neural Networks Using Latent Space Combinatorial Testing
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Seed Selection for Testing Deep Neural Networks
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Towards Feature-Based Analysis of the Machine Learning Development Lifecycle
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Automated Test Suite Generation for Software Product Lines Based on Quality-Diversity Optimization
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Systematic review of test case generation strategies for deep learning systems
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