A survey on adaptive random testing

Rubing Huang; Weifeng Sun; Yinyin Xu; Haibo Chen; Dave Towey; Xin Xia

doi:10.1109/TSE.2019.2942921

A survey on adaptive random testing

Rubing Huang, Weifeng Sun, Yinyin Xu, Haibo Chen, Dave Towey, Xin Xia

School of Computer Science

Research output: Journal Publication › Article › peer-review

49 Citations (Scopus)

90 Downloads (Pure)

Abstract

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.

Original language	English
Pages (from-to)	1-1
Journal	IEEE Transactions on Software Engineering
DOIs	https://doi.org/10.1109/TSE.2019.2942921
Publication status	Published - 23 Sept 2019

Keywords

Adaptive random testing
random testing
survey

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A survey on adaptive random testing

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Keywords

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