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pro vyhledávání: '"Tian, Yongqiang"'
Given a list L of elements and a property that L exhibits, ddmin is a well-known test input minimization algorithm designed to automatically eliminate irrelevant elements from L. This algorithm is extensively adopted in test input minimization and so
Externí odkaz:
http://arxiv.org/abs/2408.04735
Autor:
Shen, Qingchao, Tian, Yongqiang, Ma, Haoyang, Chen, Junjie, Huang, Lili, Fu, Ruifeng, Cheung, Shing-Chi, Wang, Zan
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 stages, but rarely explore bug detection at the model loading stage. Ef
Externí odkaz:
http://arxiv.org/abs/2407.16626
Publikováno v:
ISSTA 2024
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 performing compilation and optimization of Solidity code. The correctn
Externí odkaz:
http://arxiv.org/abs/2407.05981
Autor:
Tian, Yongqiang, Usachev, Alexandr
We study extended zeta-function residues on principal ideals of compact operators and their connections with Dixmier traces. We establish a Lidskii-type formula for continuous singular traces on these ideals. Using this formula, we obtain a necessary
Externí odkaz:
http://arxiv.org/abs/2407.05296
Testing is a major approach to ensuring the quality of deep learning (DL) libraries. Existing testing techniques commonly adopt differential testing to relieve the need for test oracle construction. However, these techniques are limited in finding im
Externí odkaz:
http://arxiv.org/abs/2406.07944
Program reduction is a prevalent technique to facilitate compilers' debugging by automatically minimizing bug-triggering programs. Existing program reduction techniques are either generic across languages (e.g., Perses and Vulcan) or specifically cus
Externí odkaz:
http://arxiv.org/abs/2312.13064
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 and multilingual SDK generation scheme to improve the comprehensibility
Externí odkaz:
http://arxiv.org/abs/2303.13828
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 parameters that need to be entered. However, it is challenging for user
Externí odkaz:
http://arxiv.org/abs/2304.06692
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 been proposed to automatically generate high-quality API documentation b
Externí odkaz:
http://arxiv.org/abs/2303.13041
Publikováno v:
ISSTA 2023
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 compiled DL models. A recent bug study shows that the optimization of hig
Externí odkaz:
http://arxiv.org/abs/2208.02193