Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Lou, Yiling"'
Autor:
Li, Xinyue, Chen, Zhenpeng, Zhang, Jie M., Lou, Yiling, Li, Tianlin, Sun, Weisong, Liu, Yang, Liu, Xuanzhe
Large Language Models (LLMs) have become foundational in modern language-driven applications, profoundly influencing daily life. A critical technique in leveraging their potential is role-playing, where LLMs simulate diverse roles to enhance their re
Externí odkaz:
http://arxiv.org/abs/2411.00585
Code translation converts code from one programming language to another while maintaining its original functionality, which is crucial for software migration, system refactoring, and cross-platform development. Traditional rule-based methods rely on
Externí odkaz:
http://arxiv.org/abs/2409.19894
Autor:
Liu, Junwei, Wang, Kaixin, Chen, Yixuan, Peng, Xin, Chen, Zhenpeng, Zhang, Lingming, Lou, Yiling
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the cap
Externí odkaz:
http://arxiv.org/abs/2409.02977
Python's dynamic typing system offers flexibility and expressiveness but can lead to type-related errors, prompting the need for automated type inference to enhance type hinting. While existing learning-based approaches show promising inference accur
Externí odkaz:
http://arxiv.org/abs/2407.02095
Autor:
Du, Xueying, Zheng, Geng, Wang, Kaixin, Feng, Jiayi, Deng, Wentai, Liu, Mingwei, Chen, Bihuan, Peng, Xin, Ma, Tao, Lou, Yiling
Vulnerability detection is essential for software quality assurance. In recent years, deep learning models (especially large language models) have shown promise in vulnerability detection. In this work, we propose a novel LLM-based vulnerability dete
Externí odkaz:
http://arxiv.org/abs/2406.11147
Repository-level code completion is challenging as it involves complicated contexts from multiple files in the repository. To date, researchers have proposed two technical categories to enhance LLM-based repository-level code completion, i.e., retrie
Externí odkaz:
http://arxiv.org/abs/2406.10018
Autor:
Qin, Yihao, Wang, Shangwen, Lou, Yiling, Dong, Jinhao, Wang, Kaixin, Li, Xiaoling, Mao, Xiaoguang
Fault Localization (FL) is an essential step during the debugging process. With the strong capabilities of code comprehension, the recent Large Language Models (LLMs) have demonstrated promising performance in diagnosing bugs in the code. Nevertheles
Externí odkaz:
http://arxiv.org/abs/2403.16362
Crash bugs cause unexpected program behaviors or even termination, requiring high-priority resolution. However, manually resolving crash bugs is challenging and labor-intensive, and researchers have proposed various techniques for their automated loc
Externí odkaz:
http://arxiv.org/abs/2312.10448
Resource leaks, caused by resources not being released after acquisition, often lead to performance issues and system crashes. Existing static detection techniques rely on mechanical matching of predefined resource acquisition/release APIs and null-c
Externí odkaz:
http://arxiv.org/abs/2311.04448
Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that
Externí odkaz:
http://arxiv.org/abs/2308.11422