A survey on adaptive random testing

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

Research output: Journal PublicationArticlepeer-review

15 Citations (Scopus)
3 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 languageEnglish
Pages (from-to)1-1
JournalIEEE Transactions on Software Engineering
DOIs
Publication statusPublished - 23 Sep 2019

Keywords

  • Adaptive random testing
  • random testing
  • survey

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