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Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems

https://doi.org/10.1145/3569935
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Fast Effective Rule Induction
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Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Systematic Mapping Study of the Metrics, Uses and Subjects of Diversity‐Based Testing Techniques
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DevOps Testing for Cyber-Physical Systems
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An industrial experience report on applying search-based boundary input generation to cyber-physical systems
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XMutant: XAI-based fuzzing for deep learning systems
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Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature review
inferred← citeshttps://doi.org/10.1051/sands/2024020openalex-snapshot
A requirements model for AI algorithms in functional safety-critical systems with an explainable self-enforcing network from a developer perspective
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From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems
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Search-Based DNN Testing and Retraining With GAN-Enhanced Simulations
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ThirdEye: Attention Maps for Safe Autonomous Driving Systems
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Seed Selection for Testing Deep Neural Networks
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Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches
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StaRFuzz: Stability-Aware Fuzzing for Deep Neural Networks via Multi-Granularity Internal Responses
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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