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Environmental Modeling for Automated Cloud Application Testing
March-April 2012 (vol. 29 no. 2)
pp. 30-35
| ASCII Text | x | ||
| Linghao Zhang, Xiaoxing Ma, Jian Lu, Tao Xie, Nikolai Tillmann, Peli de Halleux, "Environmental Modeling for Automated Cloud Application Testing," IEEE Software, vol. 29, no. 2, pp. 30-35, March-April, 2012. | |||
| BibTex | x | ||
| @article{ 10.1109/MS.2011.158, author = {Linghao Zhang and Xiaoxing Ma and Jian Lu and Tao Xie and Nikolai Tillmann and Peli de Halleux}, title = {Environmental Modeling for Automated Cloud Application Testing}, journal ={IEEE Software}, volume = {29}, number = {2}, issn = {0740-7459}, year = {2012}, pages = {30-35}, doi = {http://doi.ieeecomputersociety.org/10.1109/MS.2011.158}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Software TI - Environmental Modeling for Automated Cloud Application Testing IS - 2 SN - 0740-7459 SP30 EP35 EPD - 30-35 A1 - Linghao Zhang, A1 - Xiaoxing Ma, A1 - Jian Lu, A1 - Tao Xie, A1 - Nikolai Tillmann, A1 - Peli de Halleux, PY - 2012 KW - cloud computing KW - software testing KW - dynamic symbolic execution KW - cloud environment model KW - software engineering VL - 29 JA - IEEE Software ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MS.2011.158
Platforms such as Windows Azure let applications conduct data-intensive cloud computing. Unit testing can help ensure high-quality development of such applications, but the results depend on test inputs and the cloud environment's state. Manually providing various test inputs and cloud states is laborious and time-consuming. However, automated test generation must simulate various cloud states to achieve effective testing. To address this challenge, a proposed approach models the cloud environment and applies dynamic symbolic execution to generate test inputs and cloud states. Applying this approach to open-source Azure cloud applications shows that it can achieve high structural coverage.
1. P. Godefroid, N. Klarlund, and K. Sen, "DART: Directed Automated Random Testing," Proc. 2005 ACM SIGPLAN Conf. Programming Language Design and Implementation (PLID 05), ACM, 2005, pp. 213–223.
1. M. Fowler, "Mocks Aren't Stubs,"2 Jan. 2007; www.martinfowler.com/articlesmocksArentStubs.html .
2. N. Tillmann and W. Schulte, "Mock-Object Generation with Behavior," Proc. 21st IEEE/ACM Int'l Conf. Automated Software Eng. (ASE 06), IEEE CS, 2006, pp. 365–368.
3. K. Taneja, Y. Zhang, and T. Xie, "MODA: Automated Test Generation for Database Applications via Mock Objects," Proc. 25th IEEE/ACM Int'l Conf. Automated Software Eng. (ASE 10), IEEE CS, 2010, pp. 289–292.
Index Terms:
cloud computing, software testing, dynamic symbolic execution, cloud environment model, software engineering
Citation:
Linghao Zhang, Xiaoxing Ma, Jian Lu, Tao Xie, Nikolai Tillmann, Peli de Halleux, "Environmental Modeling for Automated Cloud Application Testing," IEEE Software, vol. 29, no. 2, pp. 30-35, March-April 2012, doi:10.1109/MS.2011.158
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