Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Chen, Zimin"'
Software optimization refines programs for resource efficiency while preserving functionality. Traditionally, it is a process done by developers and compilers. This paper introduces a third option, automated optimization at the source code level. We
Externí odkaz:
http://arxiv.org/abs/2309.14846
Bug datasets are vital for enabling deep learning techniques to address software maintenance tasks related to bugs. However, existing bug datasets suffer from precise and scale limitations: they are either small-scale but precise with manual validati
Externí odkaz:
http://arxiv.org/abs/2309.06229
Autor:
Chen, Zimin, Salawa, Malgorzata, Vijayvergiya, Manushree, Petrovic, Goran, Ivankovic, Marko, Just, Rene
Diff-based mutation testing is a mutation testing approach that only mutates lines affected by a code change under review. Google's mutation testing service integrates diff-based mutation testing into the code review process and continuously gathers
Externí odkaz:
http://arxiv.org/abs/2306.09130
Autor:
Chen, Shujian, Chen, Zimin, Chen, Weiqu, Fang, Paiwen, Liang, Jun, Wang, Xinzhong, Wang, Gang, Pei, Yanli
Publikováno v:
In Journal of Alloys and Compounds 25 June 2024 989
Publikováno v:
In Science of the Total Environment 1 May 2024 923
Publikováno v:
Proceedings of International Conference on Software Maintenance and Evolution, 2021
Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in
Externí odkaz:
http://arxiv.org/abs/2107.00992
Autor:
Dong, Youming, Gao, Minling, Cai, Qiqi, Qiu, Weiwen, Xiao, Ling, Chen, Zimin, Peng, Hongchang, Liu, Qinghai, Song, Zhengguo
Publikováno v:
In Journal of Hazardous Materials 5 March 2024 465
Autor:
Liu, Wanyi, Tang, Ziheng, Chen, Zimin, Li, Zijie, Jiang, Xiaoyu, Shen, Zhixian, Tan, Lei, Liu, Wenzi, Zeng, Zhenling, Shen, Xiangguang
Publikováno v:
In Microchemical Journal February 2024 197
Publikováno v:
IEEE Transactions on Software Engineering, 2022
In this paper, we address the problem of automatic repair of software vulnerabilities with deep learning. The major problem with data-driven vulnerability repair is that the few existing datasets of known confirmed vulnerabilities consist of only a f
Externí odkaz:
http://arxiv.org/abs/2104.08308
Autor:
Baudry, Benoit, Chen, Zimin, Etemadi, Khashayar, Fu, Han, Ginelli, Davide, Kommrusch, Steve, Martinez, Matias, Monperrus, Martin, Ron, Javier, Ye, He, Yu, Zhongxing
Publikováno v:
IEEE Software, 2021
Software bugs are common and correcting them accounts for a significant part of costs in the software development and maintenance process. This calls for automatic techniques to deal with them. One promising direction towards this goal is gaining rep
Externí odkaz:
http://arxiv.org/abs/2012.06824