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WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A generalization of the beta distribution with applications
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Effective test generation using pre-trained Large Language Models and mutation testing
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ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES
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LLVM: a compilation framework for lifelong program analysis & transformation
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Grey-Box Concolic Testing on Binary Code
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CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
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CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models
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Fuzzing Automatic Differentiation in Deep-Learning Libraries
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ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation
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Finding and understanding bugs in C compilers
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Coverage is not strongly correlated with test suite effectiveness
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Compiler validation via equivalence modulo inputs
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Finding deep compiler bugs via guided stochastic program mutation
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Toward understanding compiler bugs in GCC and LLVM
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Skeletal program enumeration for rigorous compiler testing
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Automated testing of graphics shader compilers
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Deep learning library testing via effective model generation
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Learning input tokens for effective fuzzing
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Random testing for C and C++ compilers with YARPGen
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Test-case reduction and deduplication almost for free with transformation-based compiler testing
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Coverage-guided tensor compiler fuzzing with joint IR-pass mutation
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NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
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DSFuzz: Detecting Deep State Bugs with Dependent State Exploration
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Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
inferredcites →https://doi.org/10.1145/3597926.3598130openalex-snapshot
GrayC: Greybox Fuzzing of Compilers and Analysers for C
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Symbolic execution and program testing
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NeuRI: Diversifying DNN Generation via Inductive Rule Inference
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.1109/compsac65507.2025.00335openalex-snapshot
White-box Fuzzing in the Wild: A Chaos Engineering Module for DevOps Security Education
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On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations
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The Mutators Reloaded: Fuzzing Compilers with Large Language Model Generated Mutation Operators
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A Pilot Study in Surveying Data Challenges of Automatic Software Engineering Tasks
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On the Practicality of LLM-Based Compiler Fuzzing
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