14 papers · 1 filter
Agentic Proof and Property-Based Testing via Property-Templates in Data-Intensive Computing
Seongmin Lee, Yaoxuan Wu, Miryung Kim
As the cost of code generation becomes cheaper with AI, the new bottleneck in software engineering has shifted to intent specification and validation. Overcoming this durability cr…
Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers
Yuxin Qiu, Ben Limpanukorn, Seongmin Lee +3
Deep learning (DL) compilers such as TVM and ONNX-MLIR lower tensor computation graphs into optimized executables for target backends. Testing these compilers has made substantial…
Finding Compiler-Platform Interaction Bugs in Deep Learning Pipelines via Cross-Layer Constraints
Yuxin Qiu, Jiyuan Wang, Ronak Badhe +3
The growing deployment of artificial intelligence (AI) necessitates robust deep learning (DL) compilers, such as TVM and ONNX-MLIR. These compilers take as input high-level AI mode…
Operationalizing Property-Based Testing for Data-Intensive Scalable Computing Systems
Yaoxuan Wu, Ingrid Lee, Ahmad Humayun +2
While fuzzing effectively catches crashes, its shallow oracles often miss semantic drifts and optimization-related errors in data-intensive scalable computing (DISC) frameworks. Pr…
ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits
Annaëlle Baiget, Jaron Maene, Seongmin Lee +3
Understanding and explaining the structure of generated test inputs is essential for effective software testing and debugging. Existing approaches--including grammar-based fuzzers,…
PALM: Path-aware LLM-based Test Generation with Comprehension
Yaoxuan Wu, Xiaojie Zhou, Ahmad Humayun +2
Symbolic execution is a widely used technique for test generation, offering systematic exploration of program paths through constraint solving. However, it is fundamentally constra…