Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Shi Jieke"'
CodeLLMs have demonstrated remarkable advancements in software engineering tasks. However, while these models can generate functionally correct code, they often produce code that is inefficient in terms of runtime. This inefficiency is particularly p
Externí odkaz:
http://arxiv.org/abs/2412.17264
With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensuring the safety of neural policies is to deploy programmatic runtime s
Externí odkaz:
http://arxiv.org/abs/2410.05641
Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input
Externí odkaz:
http://arxiv.org/abs/2410.04986
The availability of vast amounts of publicly accessible data of source code and the advances in modern language models, coupled with increasing computational resources, have led to a remarkable surge in the development of large language models for co
Externí odkaz:
http://arxiv.org/abs/2405.16746
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to
Externí odkaz:
http://arxiv.org/abs/2404.04566
Given large-scale source code datasets available in open-source projects and advanced large language models, recent code models have been proposed to address a series of critical software engineering tasks, such as program repair and code completion.
Externí odkaz:
http://arxiv.org/abs/2310.01166
Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerful models, there remain several scenarios where developers cannot take
Externí odkaz:
http://arxiv.org/abs/2309.04076
The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of lines of code
Externí odkaz:
http://arxiv.org/abs/2308.09932
Autor:
Yang, Zhou, Wang, Chenyu, Shi, Jieke, Hoang, Thong, Kochhar, Pavneet, Lu, Qinghua, Xing, Zhenchang, Lo, David
Artificial Intelligence systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, an
Externí odkaz:
http://arxiv.org/abs/2303.09795
Autor:
Yang, Zhou, Shi, Jieke, Asyrofi, Muhammad Hilmi, Xu, Bowen, Zhou, Xin, Han, DongGyun, Lo, David
With the wide adoption of automated speech recognition (ASR) systems, it is increasingly important to test and improve ASR systems. However, collecting and executing speech test cases is usually expensive and time-consuming, motivating us to strategi
Externí odkaz:
http://arxiv.org/abs/2302.00330