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Coverage-guided tensor compiler fuzzing with joint IR-pass mutation
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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/rob.21918openalex-snapshot
A survey of deep learning techniques for autonomous driving
inferredcites →https://doi.org/10.1109/cvpr.2019.01225openalex-snapshot
PifPaf: Composite Fields for Human Pose Estimation
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/ipdps.2014.110openalex-snapshot
Anatomy of High-Performance Many-Threaded Matrix Multiplication
inferredcites →https://doi.org/10.1109/mcse.2010.118openalex-snapshot
Cython: The Best of Both Worlds
inferredcites →https://doi.org/10.1109/secdev.2016.043openalex-snapshot
Continuous Fuzzing with libFuzzer and AddressSanitizer
inferredcites →https://doi.org/10.1109/sp40000.2020.00067openalex-snapshot
Fuzzing JavaScript Engines with Aspect-preserving Mutation
inferredcites →https://doi.org/10.1109/tpami.2019.2929257openalex-snapshot
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
inferredcites →https://doi.org/10.1109/tpds.2003.1214317openalex-snapshot
Tiling, block data layout, and memory hierarchy performance
inferredcites →https://doi.org/10.1109/tpds.2020.3030548openalex-snapshot
The Deep Learning Compiler: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/tse.2017.2785841openalex-snapshot
Coverage-Based Greybox Fuzzing as Markov Chain
inferredcites →https://doi.org/10.1109/tse.2019.2946563openalex-snapshot
The Art, Science, and Engineering of Fuzzing: A Survey
inferredcites →https://doi.org/10.1109/tvcg.2017.2744878openalex-snapshot
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
inferredcites →https://doi.org/10.1137/0613026openalex-snapshot
Efficient Matrix Multiplication on SIMD Computers
inferredcites →https://doi.org/10.1145/1993498.1993532openalex-snapshot
Finding and understanding bugs in C compilers
inferredcites →https://doi.org/10.1145/2568225.2568278openalex-snapshot
Code coverage for suite evaluation by developers
inferredcites →https://doi.org/10.1145/2666356.2594334openalex-snapshot
Compiler validation via equivalence modulo inputs
inferredcites →https://doi.org/10.1145/3062341.3062379openalex-snapshot
Skeletal program enumeration for rigorous compiler testing
inferredcites →https://doi.org/10.1145/3194085.3194087openalex-snapshot
Deep learning for self-driving cars
inferredcites →https://doi.org/10.1145/3238147.3238176openalex-snapshot
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
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/10.1145/3315508.3329973openalex-snapshot
Triton: an intermediate language and compiler for tiled neural network computations
inferredcites →https://doi.org/10.1145/3360607openalex-snapshot
Coverage guided, property based testing
inferredcites →https://doi.org/10.1145/3368089.3409748openalex-snapshot
Boosting fuzzer efficiency: an information theoretic perspective
inferredcites →https://doi.org/10.1145/3368089.3409761openalex-snapshot
Deep learning library testing via effective model generation
inferredcites →https://doi.org/10.1145/3372297.3417260openalex-snapshot
SQUIRREL: Testing Database Management Systems with Language Validity and Coverage Feedback
inferredcites →https://doi.org/10.1145/3377811.3380397openalex-snapshot
A comprehensive study of autonomous vehicle bugs
inferredcites →https://doi.org/10.1145/3453483.3454106openalex-snapshot
AKG: automatic kernel generation for neural processing units using polyhedral transformations
inferredcites →https://doi.org/10.1145/3510003.3510174openalex-snapshot
One fuzzing strategy to rule them all
inferredcites →https://doi.org/10.14722/ndss.2020.24018openalex-snapshot
HFL: Hybrid Fuzzing on the Linux Kernel
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.5281/zenodo.6371290openalex-snapshot
Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-97-3973-8_10openalex-snapshot
Improving Search Space Analysis of Fuzzing Mutators Using Cryptographic Structures
inferred← citeshttps://doi.org/10.1007/s11432-023-4301-6openalex-snapshot
Effective random test generation for deep learning compilers
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103903openalex-snapshot
Vulnerability detection through machine learning-based fuzzing: A systematic review
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104660openalex-snapshot
Fuzzing JavaScript JIT compilers with a high-quality differential test oracle
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00120openalex-snapshot
MLIRSmith: Random Program Generation for Fuzzing MLIR Compiler Infrastructure
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00196openalex-snapshot
Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
inferred← citeshttps://doi.org/10.1109/compsac69091.2026.00470openalex-snapshot
CovCraft: LLM-Guided Intelligent Framework for Constraint-Based Testing of Deep Learning Compiler Pipelines
inferred← citeshttps://doi.org/10.1109/eeiss62553.2024.00007openalex-snapshot
Python Coverage Guided Fuzzing for Deep Learning Framework
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00045openalex-snapshot
Evaluating and Improving Hybrid Fuzzing
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00105openalex-snapshot
Fuzzing Automatic Differentiation in Deep-Learning Libraries
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00154openalex-snapshot
Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests
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/icse55347.2025.00130openalex-snapshot
IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00175openalex-snapshot
Clozemaster: Fuzzing Rust Compiler by Harnessing Llms for Infilling Masked Real Programs
inferred← citeshttps://doi.org/10.1109/icstw64639.2025.10962531openalex-snapshot
Exploring Robustness of Image Recognition Models on Hardware Accelerators
inferred← citeshttps://doi.org/10.1109/issre62328.2024.00039openalex-snapshot
Enhancing Black-box Compiler Option Fuzzing with LLM through Command Feedback
inferred← citeshttps://doi.org/10.1109/pcds65695.2025.00059openalex-snapshot
Multifuzz: a Collaborative Fuzzing Framework Based on Concolic Execution
inferred← citeshttps://doi.org/10.1109/qrs60937.2023.00066openalex-snapshot
DeepDiffer: Find Deep Learning Compiler Bugs via Priority-guided Differential Fuzzing
inferred← citeshttps://doi.org/10.1109/tcad.2025.3528358openalex-snapshot
Magnifier: A Chiplet Feature-Aware Test Case Generation Method for Deep Learning Accelerators
inferred← citeshttps://doi.org/10.1109/tr.2026.3702727openalex-snapshot
Accelerating Deep Learning Compiler Testing via Message-Passing Neural Network With Attention
inferred← citeshttps://doi.org/10.1109/trustcom66490.2025.00258openalex-snapshot
ORThrus: Detecting Deep Learning Compiler Bugs via Optimization Resistance Transformations
inferred← citeshttps://doi.org/10.1109/tse.2024.3461657openalex-snapshot
D3: Differential Testing of Distributed Deep Learning With Model Generation
inferred← citeshttps://doi.org/10.1145/3540250.3549085openalex-snapshot
Fuzzing deep-learning libraries via automated relational API inference
inferred← citeshttps://doi.org/10.1145/3575693.3575707openalex-snapshot
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
inferred← citeshttps://doi.org/10.1145/3597503.3623343openalex-snapshot
Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3597503.3639121openalex-snapshot
Fuzz4All: Universal Fuzzing with Large Language Models
inferred← citeshttps://doi.org/10.1145/3597926.3598053openalex-snapshot
Fuzzing Deep Learning Compilers with HirGen
inferred← citeshttps://doi.org/10.1145/3597926.3598067openalex-snapshot
Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
inferred← citeshttps://doi.org/10.1145/3597926.3598105openalex-snapshot
GenCoG: A DSL-Based Approach to Generating Computation Graphs for TVM Testing
inferred← citeshttps://doi.org/10.1145/3611643.3616332openalex-snapshot
A Generative and Mutational Approach for Synthesizing Bug-Exposing Test Cases to Guide Compiler Fuzzing
inferred← citeshttps://doi.org/10.1145/3611643.3616337openalex-snapshot
NeuRI: Diversifying DNN Generation via Inductive Rule Inference
inferred← citeshttps://doi.org/10.1145/3678722.3685529openalex-snapshot
The Havoc Paradox in Generator-Based Fuzzing (Registered Report)
inferred← citeshttps://doi.org/10.1145/3688838openalex-snapshot
History-Driven Fuzzing for Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3689736openalex-snapshot
WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
inferred← citeshttps://doi.org/10.1145/3689757openalex-snapshot
PolyJuice: Detecting Mis-compilation Bugs in Tensor Compilers with Equality Saturation Based Rewriting
inferred← citeshttps://doi.org/10.1145/3705308openalex-snapshot
S cuzer : A Scheduling Optimization Fuzzer for TVM
inferred← citeshttps://doi.org/10.1145/3713081.3731731openalex-snapshot
Kitten: A Simple Yet Effective Baseline for Evaluating LLM-Based Compiler Testing Techniques
inferred← citeshttps://doi.org/10.1145/3715747openalex-snapshot
DuoReduce: Bug Isolation for Multi-layer Extensible Compilation
inferred← citeshttps://doi.org/10.1145/3716497openalex-snapshot
Deep Learning Library Testing: Definition, Methods and Challenges
inferred← citeshttps://doi.org/10.1145/3721146.3721943openalex-snapshot
Verifying Semantic Equivalence of Large Models with Equality Saturation
inferred← citeshttps://doi.org/10.1145/3729372openalex-snapshot
Directed Testing in MLIR: Unleashing Its Potential by Overcoming the Limitations of Random Fuzzing
inferred← citeshttps://doi.org/10.1145/3729533openalex-snapshot
Evaluating API-Level Deep Learning Fuzzers: A Comprehensive Benchmarking Study
inferred← citeshttps://doi.org/10.1145/3742894openalex-snapshot
The Havoc Paradox in Generator-Based Fuzzing
inferred← citeshttps://doi.org/10.1145/3744916.3773216openalex-snapshot
Optimization-Aware Test Generation for Deep Learning Compilers
inferred← citeshttps://doi.org/10.1145/3773993openalex-snapshot
Citadel : Context Similarity Based Deep Learning Framework Bug Finding
inferred← citeshttps://doi.org/10.1145/3774889openalex-snapshot
False-Positive Bug Reports in Deep Learning Compilers: Stages, Root Causes, and Mitigation
inferred← citeshttps://doi.org/10.1145/3803437.3806417openalex-snapshot
A Practical Fuzzer for the Python Runtime System
inferred← citeshttps://doi.org/10.1145/3808268openalex-snapshot
Semantic Reification: A New Paradigm for Random Program Generation
inferred← citeshttps://doi.org/10.1145/3832783.3834405openalex-snapshot
EMERGE: Checking Implementation Equivalence for LLMs via Rule Synthesis
inferred← citeshttps://doi.org/10.14722/ndss.2023.24103openalex-snapshot
OBSan: An Out-Of-Bound Sanitizer to Harden DNN Executables
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4845487/v1openalex-snapshot
Lightweight Code Coverage Analysis for Deep Learning Framework Testing
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