IEEE Access 8: 52475-52488 (2020)

Exploiting the Largest Available Zone:
A Proactive Approach to Adaptive Random Testing by Exclusion

Jinfu Chen 2 , Qihao Bao 3 , T.H. Tse 4 , Tsong Yueh Chen 5 ,
Jiaxiang Xi 3 , Chengying Mao 6 , Minjie Yu 3 , and Rubing Huang 2

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Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as "RRT by largest available zone'' (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied.
1. This work was supported in part by the National Natural Science Foundation of China under Grant U1836116, Grant 61762040, and Grant 61872167, and in part by the project of Jiangsu Provincial Six Talent Peaks under Grant XYDXXJS-016.
2. (Corresponding author.)
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212000, China.
3. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212000, China.
4. Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong.
5. Department of Computer Science and Software Engineering, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, Australia.
6. School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.


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