18 citations · 18 across the 8 of their papers we have counts for
10 papers
Are We Testing the Wrong Quantum Bugs? A Study of Fault Representation Gaps in Quantum Software Testing
Asif Mohammed Saad, Ahsanul Ameen Sabit, Jonah Brandenburgh +3
Quantum software relies on compilers, intermediate representations, and execution frameworks that transform programs before execution. Faults in these components can change the beh…
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…
Isolating Recurring Backend-Dependent Abnormal Patterns on NISQ Quantum Devices
Zhenyu Qi, Haotang Li, Mominul Islam +3
Quantum hardware remains highly susceptible to noise, making compilation a critical step for obtaining reliable execution on existing devices. Today's quantum compilers approximate…
Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits
Haichao Zhu, Qian Zhang, Jiyuan Wang +2
AI coding agents can now complete complex programming tasks, but existing evaluations largely emphasize behavioral correctness and often overlook maintainability risks such as weak…
Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles
Zitong Zhou, Ben Limpanukorn, Hong Jin Kang +5
Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heu…
PerfGen: Automated Performance Benchmark Generation for Big Data Analytics
Jiyuan Wang, Jason Teoh, Muhammand Ali Gulza +2
Many symptoms of poor performance in big data analytics such as computational skews, data skews, and memory skews are input dependent. However, due to the lack of inputs that can t…
Fuzzing MLIR Compilers with Custom Mutation Synthesis
Ben Limpanukorn, Jiyuan Wang, Hong Jin Kang +2
Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Represent…
Efficient Fuzz Testing for Apache Spark Using Framework Abstraction
Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar +2
The emerging data-intensive applications are increasingly dependent on data-intensive scalable computing (DISC) systems, such as Apache Spark, to process large data. Despite their…
QuanFuzz: Fuzz Testing of Quantum Program
Jiyuan Wang, Ming Gao, Yu Jiang +4
Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from tradit…