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COMET: Coverage-guided Model Generation For Deep Learning Library Testing

https://doi.org/10.1145/3583566
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-59410-7_40openalex-snapshot
An Empirical Study on Bugs Inside TensorFlow
inferredcites →https://doi.org/10.1007/978-3-319-46478-7_39openalex-snapshot
Information Bottleneck Domain Adaptation with Privileged Information for Visual Recognition
inferredcites →https://doi.org/10.1007/s10664-023-10389-6openalex-snapshot
Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow
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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
inferredcites →https://doi.org/10.1016/j.infsof.2022.107004openalex-snapshot
A comprehensive empirical study on bug characteristics of deep learning frameworks
inferredcites →https://doi.org/10.1016/j.jss.2021.110935openalex-snapshot
The symptoms, causes, and repairs of bugs inside a deep learning library
inferredcites →https://doi.org/10.1016/j.media.2017.07.005openalex-snapshot
A survey on deep learning in medical image analysis
inferredcites →https://doi.org/10.1016/j.scs.2020.102589openalex-snapshot
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/ase.2019.00078openalex-snapshot
A Study of Oracle Approximations in Testing Deep Learning Libraries
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/icc.2018.8422832openalex-snapshot
A Deep Learning Approach to IoT Authentication
inferredcites →https://doi.org/10.1109/iccv.2015.312openalex-snapshot
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
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/icse.2019.00126openalex-snapshot
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
inferredcites →https://doi.org/10.1109/icse43902.2021.00034openalex-snapshot
DeepLocalize: Fault Localization for Deep Neural Networks
inferredcites →https://doi.org/10.1109/icse43902.2021.00037openalex-snapshot
Graph-Based Fuzz Testing for Deep Learning Inference Engines
inferredcites →https://doi.org/10.1109/icse43902.2021.00138openalex-snapshot
Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
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/issre.2018.00021openalex-snapshot
DeepMutation: Mutation Testing of Deep Learning Systems
inferredcites →https://doi.org/10.1109/tcomm.2018.2878025openalex-snapshot
Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems
inferredcites →https://doi.org/10.1109/tse.2004.24openalex-snapshot
Software fault interactions and implications for software testing
inferredcites →https://doi.org/10.1145/1297846.1297902openalex-snapshot
Randoop
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EvoSuite
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Guided differential testing of certificate validation in SSL/TLS implementations
inferredcites →https://doi.org/10.1145/2908080.2908095openalex-snapshot
Coverage-directed differential testing of JVM implementations
inferredcites →https://doi.org/10.1145/2950290.2950355openalex-snapshot
Detecting and fixing precision-specific operations for measuring floating-point errors
inferredcites →https://doi.org/10.1145/2980983.2908095openalex-snapshot
Coverage-directed differential testing of JVM implementations
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3238147.3238187openalex-snapshot
DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems
inferredcites →https://doi.org/10.1145/3324884.3416571openalex-snapshot
Audee
inferredcites →https://doi.org/10.1145/3361566openalex-snapshot
DeepXplore
inferredcites →https://doi.org/10.1145/3368089.3409720openalex-snapshot
Detecting numerical bugs in neural network architectures
inferredcites →https://doi.org/10.1145/3368089.3409761openalex-snapshot
Deep learning library testing via effective model generation
inferredcites →https://doi.org/10.1145/3460319.3464825openalex-snapshot
DeepCrime: mutation testing of deep learning systems based on real faults
inferredcites →https://doi.org/10.1145/3460319.3464843openalex-snapshot
Predoo: precision testing of deep learning operators
inferredcites →https://doi.org/10.1145/3468264.3468591openalex-snapshot
A comprehensive study of deep learning compiler bugs
inferredcites →https://doi.org/10.1145/3510003.3510041openalex-snapshot
Free lunch for testing
inferredcites →https://doi.org/10.1145/3510003.3510092openalex-snapshot
Muffin
inferredcites →https://doi.org/10.1145/3533767.3534220openalex-snapshot
DocTer: documentation-guided fuzzing for testing deep learning API functions
inferredcites →https://doi.org/10.1145/3587155openalex-snapshot
Toward Understanding Deep Learning Framework Bugs
inferredcites →https://doi.org/10.2352/issn.2470-1173.2017.19.avm-023openalex-snapshot
Deep Reinforcement Learning framework for Autonomous Driving

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-96-1548-3_12openalex-snapshot
A Comprehensive Review on Deep Learning System Testing
inferred← citeshttps://doi.org/10.1007/s10664-023-10389-6openalex-snapshot
Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow
inferred← citeshttps://doi.org/10.1016/j.infsof.2026.108195openalex-snapshot
Effective Python-frontend fuzzing for deep learning libraries with runtime coverage feedback
inferred← citeshttps://doi.org/10.1016/j.jss.2026.112822openalex-snapshot
BCI-Fuzz: Bug-triggering code innovated to fuzz deep learning libraries
inferred← citeshttps://doi.org/10.1109/apsec66846.2025.00031openalex-snapshot
Testing Deep Learning Libraries with Semantic Equivalent API Patterns
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00086openalex-snapshot
Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration
inferred← citeshttps://doi.org/10.1109/bigdata62323.2024.10825791openalex-snapshot
A Study of Data-Path Bugs in PyTorch with a Focus on Memory Issues
inferred← citeshttps://doi.org/10.1109/dsc67331.2025.00090openalex-snapshot
Lightweight Code Coverage Analysis for Deep Learning Library Fuzzing
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00025openalex-snapshot
A Tale of Two DL Cities: When Library Tests Meet Compiler
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00202openalex-snapshot
Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning Libraries
inferred← citeshttps://doi.org/10.1109/issre62328.2024.00018openalex-snapshot
Detecting Numerical Deviations in Deep Learning Models Introduced by the TVM Compiler
inferred← citeshttps://doi.org/10.1109/qrs-c63300.2024.00070openalex-snapshot
CGFuzz: A Dynamic Test Case Generation Method for DL Framework Based on Function Coverage
inferred← citeshttps://doi.org/10.1109/qrs60937.2023.00044openalex-snapshot
Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models
inferred← citeshttps://doi.org/10.1109/trustcom66490.2025.00349openalex-snapshot
DeepLancet: Effectively Detecting Deep Learning Library Bugs via LLM-assisted Testcase Generation
inferred← citeshttps://doi.org/10.1109/tse.2025.3552622openalex-snapshot
Evaluating Spectrum-Based Fault Localization on Deep Learning Libraries
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/10.1145/3597926.3598088openalex-snapshot
ACETest: Automated Constraint Extraction for Testing Deep Learning Operators
inferred← citeshttps://doi.org/10.1145/3650212.3680374openalex-snapshot
Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters
inferred← citeshttps://doi.org/10.1145/3650212.3680386openalex-snapshot
Practitioners’ Expectations on Automated Test Generation
inferred← citeshttps://doi.org/10.1145/3660796openalex-snapshot
A Miss Is as Good as A Mile: Metamorphic Testing for Deep Learning Operators
inferred← citeshttps://doi.org/10.1145/3691620.3695478openalex-snapshot
Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
inferred← citeshttps://doi.org/10.1145/3697010openalex-snapshot
An Empirical Study of the Non-Determinism of ChatGPT in Code Generation
inferred← citeshttps://doi.org/10.1145/3716497openalex-snapshot
Deep Learning Library Testing: Definition, Methods and Challenges
inferred← citeshttps://doi.org/10.1145/3735637openalex-snapshot
Enhancing Differential Testing with LLMs for Testing Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3744916.3773216openalex-snapshot
Optimization-Aware Test Generation for Deep Learning Compilers
inferred← citeshttps://doi.org/10.1145/3744916.3773242openalex-snapshot
Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
inferred← citeshttps://doi.org/10.1145/3808174openalex-snapshot
DECODE: Dynamic Exploration for Constraint-Guided Vulnerability Discovery in Deep Learning Operators
inferred← citeshttps://doi.org/10.1145/3816024openalex-snapshot
How Do Large Language Models Perform in Deep Learning Code Generation? An Empirical Study
inferred← citeshttps://doi.org/10.1145/3832783.3834345openalex-snapshot
CIPIHunter: Detecting Configuration-Induced Prediction Instability in Deep Learning Frameworks
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4845487/v1openalex-snapshot
Lightweight Code Coverage Analysis for Deep Learning Framework Testing
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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