Research Progress in Testing Techniques of Component-Based Software

Mao Chengying and Lu Yansheng

Journal of Computer Research and Development > 2006 > 43(8): 1375-1382.

Mao Chengying and Lu Yansheng. Research Progress in Testing Techniques of Component-Based Software[J]. Journal of Computer Research and Development, 2006, 43(8): 1375-1382.

Citation:

Mao Chengying and Lu Yansheng. Research Progress in Testing Techniques of Component-Based Software[J]. Journal of Computer Research and Development, 2006, 43(8): 1375-1382.

Citation:

Mao Chengying and Lu Yansheng. Research Progress in Testing Techniques of Component-Based Software[J]. Journal of Computer Research and Development, 2006, 43(8): 1375-1382.

PDF (802 KB)

Research Progress in Testing Techniques of Component-Based Software

Mao Chengying and Lu Yansheng

(College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074)

More Information

Published Date: August 14, 2006

Graphical Abstract

Abstract

Abstract

Software component technology provides a more effective design pattern than object-oriented methodology. Component-based software has been widely used in many fields and become a fairly popular software form in recent years. However, the features of component, such as information encapsulating and high evolvability, and properties of component composition (e.g., heterogeneous, loosely coupled and interoperability, etc.) bring great challenges to the testing of component-based software systems. In this paper, the representative testing methods and techniques for component and component-based software in recent years are analyzed and surveyed. Some effective testing frameworks and tools are also summarized and compared. Furthermore, the directions of future research are explored.
- component-based software (CBS),
- testing,
- testability,
- internetware,
- reliability

FullText(HTML)

References (0)

[1]	Ren Jiadong, Liu Xinqian, Wang Qian, He Haitao, Zhao Xiaolin. An Multi-Level Intrusion Detection Method Based on KNN Outlier Detection and Random Forests[J]. Journal of Computer Research and Development, 2019, 56(3): 566-575. DOI: 10.7544/issn1000-1239.2019.20180063
[2]	Liu Lu, Zuo Wanli, Peng Tao. Tensor Representation Based Dynamic Outlier Detection Method in Heterogeneous Network[J]. Journal of Computer Research and Development, 2016, 53(8): 1729-1739. DOI: 10.7544/issn1000-1239.2016.20160178
[3]	Zhao Xingwang, Liang Jiye. An Attribute Weighted Clustering Algorithm for Mixed Data Based on Information Entropy[J]. Journal of Computer Research and Development, 2016, 53(5): 1018-1028. DOI: 10.7544/issn1000-1239.2016.20150131
[4]	Huang Tianqiang, Yu Yangqiang, Guo Gongde, Qin Xiaolin. Trajectory Outlier Detection Based on Semi-Supervised Technology[J]. Journal of Computer Research and Development, 2011, 48(11): 2074-2082.
[5]	Zhang Jing, Sun Zhihui, Yang Ming, Ni Weiwei, Yang Yidong. Fast Incremental Outlier Mining Algorithm Based on Grid and Capacity[J]. Journal of Computer Research and Development, 2011, 48(5): 823-830.
[6]	Yu Hao, Wang Bin, Xiao Gang, Yang Xiaochun. Distance-Based Outlier Detection on Uncertain Data[J]. Journal of Computer Research and Development, 2010, 47(3): 474-484.
[7]	Ni Weiwei, Chen Geng, Lu Jieping, Wu Yingjie, Sun Zhihui. Local Entropy Based Weighted Subspace Outlier Mining Algorithm[J]. Journal of Computer Research and Development, 2008, 45(7): 1189-1194.
[8]	Jin Yifu, Zhu Qingsheng, Xing Yongkang. An Algorithm for Clustering of Outliers Based on Key Attribute Subspace[J]. Journal of Computer Research and Development, 2007, 44(4): 651-659.
[9]	Ni Weiwei, Lu Jieping, Chen Geng, and Sun Zhihui. An Efficient Data Stream Outliers Detection Algorithm Based on k-Means Partitioning[J]. Journal of Computer Research and Development, 2006, 43(9): 1639-1643.
[10]	Yang Yidong, Sun Zhihui, Zhang Jing. Finding Outliers in Distributed Data Streams Based on Kernel Density Estimation[J]. Journal of Computer Research and Development, 2005, 42(9): 1498-1504.