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Lattice-based adaptive random testing
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Source Automated Software Engineering archive
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering table of contents
Long Beach, CA, USA
SESSION: Short papers 1 table of contents
Pages: 333 - 336  
Year of Publication: 2005
ISBN:1-59593-993-4
Author
Johannes Mayer  University of Ulm, Ulm, Germany
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 8,   Downloads (12 Months): 57,   Citation Count: 11
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ABSTRACT

Adaptive Random Testing (ART) denotes a family of testing algorithms that have a better performance compared to pure random testing with respect to the number of test cases necessary to detect the first failure. Many of these algorithms, however, are not very efficient regarding runtime. A new ART algorithm is presented that has a better performance than all other ART methods for the block failure pattern. Its runtime is linear in the number of test cases selected, which is nearly as efficient as pure random testing, as opposed to most other ART methods. This new ART algorithm selects the test cases based on a lattice.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
V. D. Agrawal. When to use random testing. IEEE Transactions on Computers, 27:1054--1055, 1978.
 
2
G. Casella and R. L. Berger. Statistical Inference. Wadsworth Group, Duxbury, CA, USA, 2002.
 
3
F. T. Chan, T. Y. Chen, I. K. Mak, and Y. T. Yu. Proportional sampling strategy: Guidelines for software testing practitioners. Information and Software Technology, 38:775--782, 1996.
 
4
 
5
 
6
K. P. Chan, T. Y. Chen, and D. Towey. Normalized restricted random testing. In Proceedings of the 18th Ada-Europe International Conference on Reliable Software Technologies, volume 2655 of Lecture Notes in Computer Science, pages 368--381. Springer, 2003.
 
7
 
8
T. Y. Chen, F.-C. Kuo, R. G. Merkel, and S. P. Ng. Mirror adaptive random testing. Information and Software Technology, 46:1001--1010, 2004.
 
9
T. Y. Chen, H. Leung, and I. K. Mak. Adaptive random testing. In M.~J. Maher, editor, Proceedings of the 9th Asian Computing Science Conference (ASIAN 2004), pages 320--329.
 
10
 
11
J. W. Duran and S. C. Ntafos. An evaluation of random testing. IEEE Transactions on Software Engineering, 10:438--444, 1984.
 
12
 
13
 
14
R. Hamlet. Random testing. In Encylopedia of Software Engineering, pages 970--978. Wiley, 1994.
 
15
P. S. Loo and W. K. Tsai. Random testing revisited. Information and Software Technology, 30:402--417, 1988.
 
16
 
17
P. B. Schneck. Comment on "when to use random testing". IEEE Transactions on Computers, 28:580--581, 1979.

CITED BY  11