42 citations · 133 across the 16 of their papers we have counts for
16 papers
Delta Debugging in the Absence of Test Oracles Through Metamorphic Testing
Mingyue Jiang, Yongqiang Tian, Tsong Yueh Chen
Delta debugging provides an automatic way to minimize a program input while preserving a certain property. However, its effectiveness fundamentally relies on the availability of te…
Optimization-Aware Test Generation for Deep Learning Compilers
Qingchao Shen, Zan Wang, Haoyang Ma +5
Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring…
Bounded Exhaustive Random Program Generation for Testing Solidity Compilers
Haoyang Ma, Alastair F. Donaldson, Qingchao Shen +3
By July 2025, smart contracts collectively manage roughly $120 billion in assets. With Solidity remaining the dominant language for smart contract development, the correctness of S…
A Tale of Two DL Cities: When Library Tests Meet Compiler
Qingchao Shen, Yongqiang Tian, Haoyang Ma +5
Deep Learning (DL) compilers typically load a DL model and optimize it with intermediate representation.Existing DL compiler testing techniques mainly focus on model optimization s…
Towards Understanding the Bugs in Solidity Compiler
Haoyang Ma, Wuqi Zhang, Qingchao Shen +3
Solidity compiler plays a key role in enabling the development of smart contract applications on Ethereum by governing the syntax of a domain-specific language called Solidity and…
Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries
Meiziniu Li, Dongze Li, Jianmeng Liu +3
Differential testing offers a promising strategy to alleviate the test oracle problem by comparing the test results between alternative implementations. However, existing different…
Improving API Documentation Comprehensibility via Continuous Optimization and Multilingual SDK
Shujun Wang, Yongqiang Tian, Dengcheng He
Optimizing and maintaining up-to-date API documentation is a challenging problem for evolving OpenAPIs. In this poster, we propose a data-driven continuous optimization solution an…
Reduce API Debugging Overhead via Knowledge Prepositioning
Shujun Wang, Yongqiang Tian, Dengcheng He
OpenAPI indicates a behavior where producers offer Application Programming Interfaces (APIs) to help end-users access their data, resources, and services. Generally, API has many p…
gDoc: Automatic Generation of Structured API Documentation
Shujun Wang, Yongqiang Tian, Dengcheng He
Generating and maintaining API documentation with integrity and consistency can be time-consuming and expensive for evolving APIs. To solve this problem, several approaches have be…
gMeta: Template-based Regular Expression Generation over Noisy Examples
Shujun Wang, Yongqiang Tian andDengcheng He
Regular expressions (regexes) are widely used in different fields of computer science, such as programming languages, string processing, and databases. However, existing tools for…
Fuzzing Deep Learning Compilers with HirGen
Haoyang Ma, Qingchao Shen, Yongqiang Tian +2
Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of com…
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
Meiziniu Li, Jialun Cao, Yongqiang Tian +3
Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications…
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Jialun Cao, Meiziniu Li, Xiao Chen +4
As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious ne…
Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications
Yongqiang Tian, Wuqi Zhang, Ming Wen +4
Model compression can significantly reduce the sizes of deep neural network (DNN) models, and thus facilitates the dissemination of sophisticated, sizable DNN models, especially fo…
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations
Yongqiang Tian, Shiqing Ma, Ming Wen +3
Deep learning models are widely used for image analysis. While they offer high performance in terms of accuracy, people are concerned about if these models inappropriately make inf…
Benchmarking NFV Software Dataplanes
Zhixiong Niu, Hong Xu, Yongqiang Tian +3
A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about…