Reusing deep learning models: Challenges and directions in software engineering
Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including
computer vision, system configuration, and question-answering. However, DNNs are …
computer vision, system configuration, and question-answering. However, DNNs are …
Interoperability in deep learning: A user survey and failure analysis of onnx model converters
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a
variety of development frameworks and runtime environments. DL model converters move …
variety of development frameworks and runtime environments. DL model converters move …
Testing the compiler for a new-born programming language: An industrial case study (experience paper)
Due to the critical role of compilers, many compiler testing techniques have been proposed,
two most notable categories among which are grammar-based and metamorphic-based …
two most notable categories among which are grammar-based and metamorphic-based …
Analysis of failures and risks in deep learning model converters: A case study in the onnx ecosystem
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a
variety of development frameworks and runtime environments. DL model converters move …
variety of development frameworks and runtime environments. DL model converters move …
MR-Scout: Automated Synthesis of Metamorphic Relations from Existing Test Cases
Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on
metamorphic relations (MRs) that govern multiple related inputs and their outputs. However …
metamorphic relations (MRs) that govern multiple related inputs and their outputs. However …
Met-mapf: A metamorphic testing approach for multi-agent path finding algorithms
The Multi-Agent Path Finding (MAPF) problem, ie, the scheduling of multiple agents to reach
their destinations, has been widely investigated. Testing MAPF systems is challenging, due …
their destinations, has been widely investigated. Testing MAPF systems is challenging, due …
Fuzzing deep learning compilers with hirgen
Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for
efficient deployment on diverse hardware. Their quality has a profound effect on the quality …
efficient deployment on diverse hardware. Their quality has a profound effect on the quality …
Metamorphic testing of secure multi-party computation (mpc) compilers
The demanding need to perform privacy-preserving computations among multiple data
owners has led to the prosperous development of secure multi-party computation (MPC) …
owners has led to the prosperous development of secure multi-party computation (MPC) …
PolyJuice: Detecting Mis-compilation Bugs in Tensor Compilers with Equality Saturation Based Rewriting
Tensor compilers are essential for deploying deep learning applications across various
hardware platforms. While powerful, they are inherently complex and present significant …
hardware platforms. While powerful, they are inherently complex and present significant …
A survey of modern compiler fuzzing
H Ma - arxiv preprint arxiv:2306.06884, 2023 - arxiv.org
Most software that runs on computers undergoes processing by compilers. Since compilers
constitute the fundamental infrastructure of software development, their correctness is …
constitute the fundamental infrastructure of software development, their correctness is …