Enhanced random testing for programs with high dimensional input domains

Authors

Fei-Ching Kuo, University of WollongongFollow
K. Y. Sim, Swinburne University of Technology
Chang-ai Sun, University of Groningen
S.-F. Tang, Swinburne University of Technology
Zhiquan Zhou, University of Groningen

RIS ID

22456

Publication Details

Kuo, F., Sim, K., Sun, C., Tang, S. & Zhou, Z. 2007, 'Enhanced random testing for programs with high dimensional input domains', International Conference on Software Engineering and Knowledge Engineering, Knowledge Systems Institute, USA, pp. 135-140.

Abstract

Random Testing (RT) is a fundamental technique of software testing. Adaptive Random Testing (ART) has recently been developed as an enhancement of RT that has better fault detection effectiveness. Several methods (algorithms) have been developed to implement ART. In most ART algorithms, however, the above enhancement diminishes when the dimensionality of the input domain increases. In this paper, we investigate the nature of failure regions in high dimensional input domains and propose enhanced random testing algorithms that improve the fault detection effectiveness of RT in high dimensional input domains.

Link to publisher version (URL)

International Conference on Software Engineering and Knowledge Engineering