| 一种随机化的软件模型生成方法 |
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| 引用本文： | 何啸,李文峰,张天,麻志毅,邵维忠,胡长军. 一种随机化的软件模型生成方法[J]. 软件学报, 2017, 28(4): 907-924 |
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| 作者姓名： | 何啸 李文峰 张天 麻志毅 邵维忠 胡长军 |
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| 作者单位： | 北京科技大学 计算机与通信工程学院, 北京 100083;北京大学高可信软件技术教育部重点实验室, 北京 100871,北京科技大学 计算机与通信工程学院, 北京 100083,南京大学计算机软件新技术国家重点实验室, 南京, 210093,北京大学高可信软件技术教育部重点实验室, 北京 100871,北京大学高可信软件技术教育部重点实验室, 北京 100871,北京科技大学 计算机与通信工程学院, 北京 100083 |
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| 基金项目： | 国家重点基础研究发展计划(2013CB329606); 国家自然科学基金(61300009). |
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| 摘 要： | 模型转换是模型驱动开发的核心技术. 当要把模型转换用于工业生产时, 其性能成为影响这一技术成败的关键因素之一. 为了测试模型转换程序的性能, 需要能够快速地生成一组具有较大规模的模型数据用于作为测试的输入数据. 本文提出一种随机化的模型生成方法. 该方法能够根据元模型的定义以及用户输入的约束条件随机、正确地生成模型文件. 实验结果也表明, 本方法和其它方法相比具有更好的生成效率, 从而更加适合支持模型转换的性能测试.
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| 关 键 词： | 模型生成 模型转换 性能测试 随机测试 模型驱动工程 |
| 收稿时间： | 2014-08-28 |
| 修稿时间： | 2015-11-18 |
Randomized Approach to Software Model Generation |
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| HE Xiao,LI Wen-Feng,ZHANG Tian,MA Zhi-Yi,SHAO Wei-Zhong and HU Chang-Jun. Randomized Approach to Software Model Generation[J]. Journal of Software, 2017, 28(4): 907-924 |
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| Authors: | HE Xiao LI Wen-Feng ZHANG Tian MA Zhi-Yi SHAO Wei-Zhong HU Chang-Jun |
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| Affiliation: | School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;Key Laboratory of High Confidence Software Technologies for the Ministry of Education(Peking University), Beijing 100871, China;State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210093, China,School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China,State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210093, China,Key Laboratory of High Confidence Software Technologies for the Ministry of Education(Peking University), Beijing 100871, China,Key Laboratory of High Confidence Software Technologies for the Ministry of Education(Peking University), Beijing 100871, China and School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China |
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| Abstract: | Model transformation is the key to model-based software engineering. When the model transformation is applied to industrial developments, its scalability becomes an important issue. To test the performance of model transformations, we must be able to generate a set of models, i.e. the test inputs, efficiently. This paper proposes a randomized approach to generating large models. This approach can produce a model randomly and correctly based on the definition of metamodel and user-defined constraints. And the evaluation result also shows that our approach is more efficient than other approaches. It is more suitable for supporting performance testing of transformations. |
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| Keywords: | Model Generation Model Transformation Performance Testing Random Testing Model-Driven Engineering |
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