IJTEEE

Open Access Journal of Scientific, Technology & Engineering Research


International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
QUICK LINKS
CURRENT PUBLICATIONS



IJTEEE >> Volume 3 - Issue 8, August 2015 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Software Product Testing Using Orthogonal Array (OA) Testing Technique

[Full Text]

 

AUTHOR(S)

Sreenivasa Pisupati

 

KEYWORDS

Keywords: OA, DOE, Parameters, Levels,TQM

 

ABSTRACT

Abstract: This document describes the Orthogonal Array (OA) technique used in software testing. Orthogonal Array help in improving the Software product testing.Orthogonal Array employs design of experiments (DOE), which is one of the most important statistical tools of Total Quality Management (TQM) for designing high quality systems at reduced cost. Some of the benefits of Orthogonal Array techniques are the productivity improvement, quality including high code coverage, reduction in the test execution cycle time and enhanced customer satisfaction. The actual essence of testing lies in designing the minimal set of test cases which can uncover the maximum number of bugs in the system and at the same time gives a comfortable feeling about the quality of the system. Orthogonal Array Testing Strategy is a kind of “Dream Come True” for the test designers to design their test cases.

 

REFERENCES

[1] Dean, J. and S. Ghemawat (2008). Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1), 107–113. ISSN 0001-0782. URL http://doi. acm.org/10.1145/1327452.1327492.

[2] Agrawal, D., S. Das, and A. El Abbadi, Big data and cloud computing: current state and future opportunities. In Proceedings of the 14th International Conference on Ex-tending Database Technology, EDBT/ICDT ’11. ACM, New York, NY, USA, 2011. ISBN 978-1-4503-0528-0. URL http://doi.acm.org/10.1145/1951365. 1951432.

[3] Jacobs, A. (2009). The pathologies of big data. Commun. ACM, 52(8), 36–44. ISSN 0001-0782. URL http://doi.acm.org/10.1145/1536616.1536632.

[4] El Akkaoui, Z., E. Zimŕnyi, J.-N. Mazón, and J. Trujillo, A model-driven framework for etl process development. In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, DOLAP ’11. ACM, New York, NY, USA, 2011. ISBN 978-1-4503-0963-9. URL http://doi.acm.org/10.1145/2064676. 2064685.

[5] Shvachko, K., H. Kuang, S. Radia, and R. Chansler, The hadoop distributed file system. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), MSST ’10. IEEE Computer Society, Washington, DC, USA, 2010. ISBN 978-1-4244-7152-2. URL http://dx.doi.org/10.1109/MSST. 2010.5496972.

[6] Hecht, R. and S. Jablonski, Nosql evaluation: A use case oriented survey. In Pro-ceedings of the 2011 International Conference on Cloud and Service Computing, CSC ’11. IEEE Computer Society, Washington, DC, USA, 2011. ISBN 978-1-4577-1635-5. URL http://dx.doi.org/10.1109/CSC.2011.6138544.

[7] Chaudhuri, S. and U. Dayal (1997). An overview of data warehousing and olap tech-nology. SIGMOD Rec., 26(1), 65–74. ISSN 0163-5808. URL http://doi.acm. org/10.1145/248603.248616.

[8] http://en.wikipedia.org/wiki/Extract,_transform,_load