Journal of Software: Evolution and Process

Journal of Software: Evolution and Process
期刊缩写:
J. Software: Evol. Process
影响因子:
2
ISSN:
print: 2047-7481
研究领域:
Computer Science-Software
h-index:
21
自引率:
10.00%
Gold OA文章占比:
14.18%
原创研究文献占比:
87.78%
SCI收录类型:
Science Citation Index Expanded (SCIE) || Scopus (CiteScore)
期刊介绍英文:
The “Journal of Software: Evolution and Process” is an archival journal that publishes high quality, state-of-the-art research and practice papers dealing with the conception, development, testing, management, quality, maintenance, and evolution of software, systems, and services, as well as the continuous improvement of processes and capabilities surrounding them. The journal continues the tradition of “The Journal of Software Maintenance and Evolution: Research and Practice” and “Software Process: Improvements and Practice”. We will therefore continue to cover the traditional topics related to software maintenance and evolution as well as software process improvement and practice. At the same time, the concept behind the journal has evolved into a unified vision that recognizes the fundamental changes and transformations that are occurring in the fields of software and systems engineering and the need for us to adapt by broadening the topics that we address and the research methods that are used coupled with the perspectives that are utilised. Fundamental changes are occurring in the variety, scale and scope of software, systems and services that are being developed from new web and mobile computing to battle theatre technologies and everything in between.
CiteScore:
CiteScoreSJRSNIPCiteScore排名
5.30.6221.143
学科
排名
百分位
大类:Computer Science
小类:Software
164 / 407
59%
发文信息
中科院SCI期刊分区
大类 小类 TOP期刊 综述期刊
4区 计算机科学
4区 计算机:软件工程 COMPUTER SCIENCE, SOFTWARE ENGINEERING
WOS期刊分区
历年影响因子
2021年1.9720
2022年2.0000
历年发表
投稿信息
出版语言:
English
出版国家(地区):
ENGLAND
审稿时长:
6-12 weeks
出版商:
John Wiley and Sons Ltd
编辑部地址:
111 RIVER ST, HOBOKEN, USA, NJ, 07030-5774

Journal of Software: Evolution and Process - 最新文献

Practitioners' expectations on automated release note generation techniques

Pub Date : 2024-02-07 DOI: 10.1002/smr.2657 Sristy Sumana Nath, Banani Roy

GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

Pub Date : 2024-02-07 DOI: 10.1002/smr.2649 Xiangping Zhang, Jianxun Liu, Min Shi
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