Randomized Model Generation for Performance Testing of Model Transformations

被引：7

作者：

He Xiao ^{[1
,2
]}

Zhang Tian ^{[3
]}

Ma Zhiyi ^{[2
,4
]}

Shao Weizhong ^{[2
,4
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China

[2] Peking Univ, Key Lab High Confidence Software Technol, Minist Educ, Beijing 100871, Peoples R China

[3] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210008, Jiangsu, Peoples R China

[4] Peking Univ, Sch Elect Engn & Comp Sci, Software Inst, Beijing 100871, Peoples R China

来源：

2014 IEEE 38TH ANNUAL INTERNATIONAL COMPUTERS, SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC) | 2014年

关键词：

Model generation; Model Transformation; Performance of Model Transformation;

D O I：

10.1109/COMPSAC.2014.103

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Mode transformation is the key to model-based software engineering. When the model transformation is applied to industrial developments, its scalability becomes an important issue, since the model to be transformed may have a large size. To test the performance of model transformations, this paper proposes a randomized approach to generating large models as test inputs. First, the paper discusses the basic requirements and constraints for performance test input generation of the model transformation. Then, the paper presents our model generation algorithm. It can generate a model having a large size randomly and correctly within a reasonable time, according to the metamodel and user-defined constraints. Finally, an evaluation is also presented. And the result shows that our approach is more suitable for generating performance test inputs compared with existing model generation approaches.

引用

页码：11 / 20

页数：10