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Generation-based Differential Fuzzing for Deep Learning Libraries
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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On the use of a similarity function for test case selection in the context of model‐based testing
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Software Security Assessment through Specification Mutations and Fault Injection
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An Empirical Study on Bugs Inside TensorFlow
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Jittor: a novel deep learning framework with meta-operators and unified graph execution
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Recent advances and clinical applications of deep learning in medical image analysis
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Human-level control through deep reinforcement learning
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Gradient-based learning applied to document recognition
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Random testing of C compilers based on test program generation by equivalence transformation
inferredcites →https://doi.org/10.1109/apsec.2010.39openalex-snapshot
An Automatic Testing Approach for Compiler Based on Metamorphic Testing Technique
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/icse-seip.2019.00016openalex-snapshot
Perception and Practices of Differential Testing
inferredcites →https://doi.org/10.1109/icse.2019.00107openalex-snapshot
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
inferredcites →https://doi.org/10.1109/icsme52107.2021.00011openalex-snapshot
The Unit Test Quality of Deep Learning Libraries: A Mutation Analysis
inferredcites →https://doi.org/10.1109/icst.2015.7102602openalex-snapshot
Prioritizing Manual Test Cases in Traditional and Rapid Release Environments
inferredcites →https://doi.org/10.1109/icst.2016.33openalex-snapshot
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
inferredcites →https://doi.org/10.1109/tnnls.2020.2979670openalex-snapshot
A Survey of the Usages of Deep Learning for Natural Language Processing
inferredcites →https://doi.org/10.1109/tr.2021.3107165openalex-snapshot
Duo: Differential Fuzzing for Deep Learning Operators
inferredcites →https://doi.org/10.1109/tse.2011.28openalex-snapshot
Invariant-Based Automatic Testing of Modern Web Applications
inferredcites →https://doi.org/10.1109/tse.2016.2532875openalex-snapshot
A Survey on Metamorphic Testing
inferredcites →https://doi.org/10.1109/tse.2018.2876433openalex-snapshot
Metamorphic Relations for Enhancing System Understanding and Use
inferredcites →https://doi.org/10.1109/tse.2019.2946563openalex-snapshot
The Art, Science, and Engineering of Fuzzing: A Survey
inferredcites →https://doi.org/10.1145/2430536.2430540openalex-snapshot
Achieving scalable model-based testing through test case diversity
inferredcites →https://doi.org/10.1145/2642937.2642963openalex-snapshot
Language fuzzing using constraint logic programming
inferredcites →https://doi.org/10.1145/2814270.2814319openalex-snapshot
Finding deep compiler bugs via guided stochastic program mutation
inferredcites →https://doi.org/10.1145/2884781.2884879openalex-snapshot
Finding and analyzing compiler warning defects
inferredcites →https://doi.org/10.1145/2976002.2976017openalex-snapshot
QuickFuzz: an automatic random fuzzer for common file formats
inferredcites →https://doi.org/10.1145/3062341.3062349openalex-snapshot
Synthesizing program input grammars
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Automated testing of graphics shader compilers
inferredcites →https://doi.org/10.1145/3194733.3194744openalex-snapshot
Improving continuous integration with similarity-based test case selection
inferredcites →https://doi.org/10.1145/3213846.3213866openalex-snapshot
An empirical study on TensorFlow program bugs
inferredcites →https://doi.org/10.1145/3324884.3418930openalex-snapshot
Generating highly-structured input data by combining search-based testing and grammar-based fuzzing
inferredcites →https://doi.org/10.1145/3338906.3338954openalex-snapshot
DeepStellar: model-based quantitative analysis of stateful deep learning systems
inferredcites →https://doi.org/10.1145/3338906.3338970openalex-snapshot
Diversity-based web test generation
inferredcites →https://doi.org/10.1145/3368089.3409679openalex-snapshot
Mining input grammars from dynamic control flow
inferredcites →https://doi.org/10.1145/3368089.3409761openalex-snapshot
Deep learning library testing via effective model generation
inferredcites →https://doi.org/10.1145/3460319.3464843openalex-snapshot
Predoo: precision testing of deep learning operators
inferredcites →https://doi.org/10.1145/3551349.3560429openalex-snapshot
Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing
inferredcites →https://doi.org/10.1145/3587155openalex-snapshot
Toward Understanding Deep Learning Framework Bugs
inferredcites →https://doi.org/10.1145/96267.96279openalex-snapshot
An empirical study of the reliability of UNIX utilities
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Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
inferredcites →https://doi.org/10.1609/aaai.v36i11.21527openalex-snapshot
ALPHAPROG: Reinforcement Generation of Valid Programs for Compiler Fuzzing
inferredcites →https://doi.org/10.48550/arxiv.1512.01274openalex-snapshot
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
inferredcites →https://doi.org/10.48550/arxiv.1708.07747openalex-snapshot
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-71436-8_13openalex-snapshot
Machine Learning-Enhanced Analysis and Design for Trustworthy Integrated Circuits
inferred← citeshttps://doi.org/10.1007/s11390-025-4745-0openalex-snapshot
ISC4DGF: Enhancing Directed Grey-Box Fuzzing with Initial Seed Corpus Generation Driven by Large Language Models
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112571openalex-snapshot
Mokav: Execution-driven differential testing with LLMs
inferred← citeshttps://doi.org/10.1016/j.jss.2026.112999openalex-snapshot
SemuFuzz: An efficient fuzzing approach for deep learning libraries based on semantic representation
inferred← citeshttps://doi.org/10.1109/airc64931.2025.11077544openalex-snapshot
Deep Learning Scalability: A Survey of Challenges, Techniques, and Future Trends
inferred← citeshttps://doi.org/10.1109/crsit69981.2026.11605347openalex-snapshot
DeepChatAFL: Research on Protocol Fuzz Testing Method Assisted by Large Models
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00204openalex-snapshot
Are LLMs Correctly Integrated into Software Systems?
inferred← citeshttps://doi.org/10.1109/tse.2024.3461657openalex-snapshot
D3: Differential Testing of Distributed Deep Learning With Model Generation
inferred← citeshttps://doi.org/10.1109/tse.2026.3651022openalex-snapshot
Deep Learning Framework Testing via Model Mutation: How Far Are We?
inferred← citeshttps://doi.org/10.1109/tse.2026.3655712openalex-snapshot
Subgraph-Oriented Testing for Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3650212.3680351openalex-snapshot
Tacoma: Enhanced Browser Fuzzing with Fine-Grained Semantic Alignment
inferred← citeshttps://doi.org/10.1145/3691620.3695478openalex-snapshot
Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
inferred← citeshttps://doi.org/10.1145/3691620.3695523openalex-snapshot
DevMuT: Testing Deep Learning Framework via Developer Expertise-Based Mutation
inferred← citeshttps://doi.org/10.1145/3696630.3728616openalex-snapshot
Test Script Repair of Deep Learning Library Testing
inferred← citeshttps://doi.org/10.1145/3708521openalex-snapshot
Metamorphic Relation Generation: State of the Art and Research Directions
inferred← citeshttps://doi.org/10.1145/3716497openalex-snapshot
Deep Learning Library Testing: Definition, Methods and Challenges
inferred← citeshttps://doi.org/10.1145/3728972openalex-snapshot
Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
inferred← citeshttps://doi.org/10.1145/3729533openalex-snapshot
Evaluating API-Level Deep Learning Fuzzers: A Comprehensive Benchmarking Study
inferred← citeshttps://doi.org/10.1145/3735637openalex-snapshot
Enhancing Differential Testing with LLMs for Testing Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3744916.3773242openalex-snapshot
Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
inferred← citeshttps://doi.org/10.1145/3808162openalex-snapshot
LLM-Assisted Input-Requirement-Aware Differential Testing of Array Programming Frameworks
inferred← citeshttps://doi.org/10.1145/3808174openalex-snapshot
DECODE: Dynamic Exploration for Constraint-Guided Vulnerability Discovery in Deep Learning Operators
inferred← citeshttps://doi.org/10.1145/3832194openalex-snapshot
SpectraDL: A Historical Issue-Driven, Test Specification-Assisted Transfer Testing Approach for Deep Learning Frameworks via LLMs
inferred← citeshttps://doi.org/10.1186/s42400-025-00532-9openalex-snapshot
Automating fuzz driver generation for deep learning libraries with large language models
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4963553/v1openalex-snapshot
Systematic Review of Fuzzing in IoT: Evaluating Techniques, Vulnerabilities, and Research Gaps
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