Unsupervised Learning for Detecting Refactoring Opportunities in Service-Oriented Applications

被引：5
|
作者：
Rodriguez, Guillermo [1 ]
Soria, Alvaro [1 ]
Teyseyre, Alfredo [1 ]
Berdun, Luis [1 ]
Campo, Marcelo [1 ]
机构：
[1] ISISTAN Res Inst CONICET UNICEN, Campus Univ,B7001BBO Tandil, Bs As, Argentina
来源：
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2016, PT II | 2016年 / 9828卷
关键词：
Service-oriented applications; Web services; Unsupervised machine learning; Web service description language; Service understandability; Software visualization;
D O I：
10.1007/978-3-319-44406-2_27
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Service-Oriented Computing (SOC) has been widely used for building distributed and enterprise-wide software applications. One major problem in this kind of applications is their growth; as size and complexity of applications increase, the probability of duplicity of code increases, among other refactoring issues. This paper proposes an unsupervised learning approach to assist software developers in detecting refactoring opportunities in service-oriented applications. The approach gathers non-refactored Web Service Description Language (WSDL) documents and applies clustering and visualization techniques to deliver a list of refactoring suggestions to start working on the refactoring process. We evaluated our approach using two real-life case-studies by using internal validity criteria for the clustering quality.
引用
收藏
页码：335 / 342
页数：8
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