Failure-detection capability analysis of implementing parallelism in adaptive random testing algorithms

@inproceedings{Huang2013FailuredetectionCA,
  title={Failure-detection capability analysis of implementing parallelism in adaptive random testing algorithms},
  author={Rubing Huang and Xiaodong Xie and Jinfu Chen and Yansheng Lu},
  booktitle={SAC},
  year={2013}
}
Adaptive random testing (ART) has been developed as an enhancement of random testing (RT) in terms of failure-detection capability, and has been widely investigated. When a given faulty program has an N-dimensional input domain (N > 1), a straightforward approach (abbreviated as ART-C) is to implement parallelism into ART algorithms, which generates one N-dimensional test case by computing each of its N coordinates independently and in parallel. Intuitively, ART-C using a specific ART algorithm… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.

Randomized Quasi-Random Testing

IEEE Transactions on Computers • 2016
View 4 Excerpts
Highly Influenced

References

Publications referenced by this paper.
Showing 1-5 of 5 references

Mirror adaptive random testing

Information & Software Technology • 2004
View 4 Excerpts
Highly Influenced

Proportional sampling strategy: a compendium and some insights

Journal of Systems and Software • 2001
View 5 Excerpts
Highly Influenced

Data Diversity: An Approach to Software Fault Tolerance

IEEE Trans. Computers • 1988
View 4 Excerpts
Highly Influenced

Restricted Random Testing: Adaptive Random Testing by Exclusion

International Journal of Software Engineering and Knowledge Engineering • 2006
View 2 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…