A Hierarchical Categorization Approach for System Operation Services

被引:2
作者
Chen, Wei [1 ]
Xu, Peixing [1 ]
Wu, Guoquan [1 ]
Dou, Wensheng [1 ]
Gao, Chushu [1 ]
Wei, Jun [1 ]
机构
[1] Chinese Acad Sci, Inst Software, Beijing, Peoples R China
来源
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017) | 2017年
基金
中国国家自然科学基金;
关键词
D O I
10.1109/ICWS.2017.84
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Operation services are reusable and shareable units of configuration code executed by configuration management tools ( CMTs), achieving continuous deployment and continuous delivery. With the prevalence of DevOps ( Development and Operations), thousands of operation services have been developed for various software systems, and they are publicly available through the online repositories of popular CMTs. However, locating and retrieving desired operation services is challenging since keyword- and tag-based search provided by a repository is with low precision. In this paper, we implement a hierarchical categorization approach based search service, named OSFinder, which searches and locates desired operation services more accurately. OSFinder first constructs a category hierarchy for operation services across multiple repositories, and then it classifies over 13,000 operation services into 90 categories based on machine learning technique, finally it provides a search for users. With OSFinder, a user can narrow down his search scope by tracking the category hierarchy in a top-down way, and then searches in a small group with keywords. The evaluation shows that OSFinder outperforms keyword- and tag-based search.
引用
收藏
页码:700 / 707
页数:8
相关论文
共 16 条
[1]  
[Anonymous], 2008, INTERACTIVE TECHNIQU, DOI DOI 10.1145/1394669.1394685
[2]   An approach to classify software maintenance requests [J].
Di Lucca, GA ;
Di Penta, M ;
Gradara, S .
INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE, PROCEEDINGS, 2002, :93-102
[3]  
Dumitru H, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P181, DOI 10.1145/1985793.1985819
[4]  
Httermann M., 2012, DEVOPS DEV
[5]   On using machine learning to automatically classify software applications into domain categories [J].
Linares-Vasquez, Mario ;
McMillan, Collin ;
Poshyvanyk, Denys ;
Grechanik, Mark .
EMPIRICAL SOFTWARE ENGINEERING, 2014, 19 (03) :582-618
[6]  
McMillan C., 2011, 2011 IEEE 27th International Conference on Software Maintenance, P343, DOI 10.1109/ICSM.2011.6080801
[7]  
Prasad A., 2005, LUCENE SEARCH ENGINE
[8]  
RightScale, 2016, 2016 STAT CLOUD REP
[9]   A survey of hierarchical classification across different application domains [J].
Silla, Carlos N., Jr. ;
Freitas, Alex A. .
DATA MINING AND KNOWLEDGE DISCOVERY, 2011, 22 (1-2) :31-72
[10]   Hierarchical text classification and evaluation [J].
Sun, AX ;
Lim, EP .
2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2001, :521-528