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Journal of Information Science and Engineering, Vol. 27 No. 4, pp. 1449-1472 (July 2011)

On Adaptive Random Testing Through Iterative Partitioning*

1Faculty of Information and Communication Technologies
2Information Technology Services
Swinburne University of Technology
Hawthorn, Victoria 3122, Australia
3School of Computer Science and Software Engineering
University of Wollongong
Wollongong, NSW 2522, Australia

Random Testing (RT) is an important and fundamental approach to testing computer software. Adaptive Random Testing (ART) has been proposed to improve the faultdetection capability of RT. ART employs the location information of successful test cases (those that have been executed but not revealed a failure) to enforce an even spread of random test cases across the input domain. Distance-based ART (D-ART) and Restriction- based ART (R-ART) are the first two ART methods, which have considerably improved the fault-detection capability of RT. Both these methods, however, require additional computation to ensure the generation of evenly spread test cases. To reduce the overhead in test case generation, we present in this paper a new ART method using the notion of iterative partitioning. The input domain is divided into equally sized cells by a grid. The grid cells are categorized into three different groups according to their relative locations to successful test cases. In this way, our method can easily identify those grid cells that are far apart from all successful test cases for test case generation. Our method significantly reduces the time complexity, while keeping the high fault-de- tection capability.

Keywords: software testing, random testing, adaptive random testing, simulation, algorithm analysis

Full Text () Retrieve PDF document (201107_16.pdf)

Received July 31, 2009; revised November 4, 2009; accepted January 5, 2010.
Communicated by Chih-Ping Chu.
* The preliminary version of this paper was presented at the 11th Ada-Europe International Conference on Reliable Software Technologies (Ada-Europe 2006). This project was partially supported by an Australian Research Council Discovery Grant (project No. DP0880295) and a Small Grant of the University of Wollongong.
+ Corresponding author.