A survey on adaptive random testing

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...

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Main Authors: Huang, Rubing, Sun, Weifeng, Xu, Yinyin, Chen, Haibo, Towey, Dave, Xia, Xin
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://eprints.nottingham.ac.uk/61352/
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author Huang, Rubing
Sun, Weifeng
Xu, Yinyin
Chen, Haibo
Towey, Dave
Xia, Xin
author_facet Huang, Rubing
Sun, Weifeng
Xu, Yinyin
Chen, Haibo
Towey, Dave
Xia, Xin
author_sort Huang, Rubing
building Nottingham Research Data Repository
collection Online Access
description 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.
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spelling nottingham-613522020-08-19T08:23:05Z https://eprints.nottingham.ac.uk/61352/ A survey on adaptive random testing Huang, Rubing Sun, Weifeng Xu, Yinyin Chen, Haibo Towey, Dave Xia, Xin 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. 2019-09-23 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/61352/1/ilovepdf_merged%20%2820%29.pdf Huang, Rubing, Sun, Weifeng, Xu, Yinyin, Chen, Haibo, Towey, Dave and Xia, Xin (2019) A survey on adaptive random testing. IEEE Transactions on Software Engineering . p. 1. ISSN 0098-5589 Adaptive random testing; random testing; survey http://dx.doi.org/10.1109/TSE.2019.2942921 doi:10.1109/TSE.2019.2942921 doi:10.1109/TSE.2019.2942921
spellingShingle Adaptive random testing; random testing; survey
Huang, Rubing
Sun, Weifeng
Xu, Yinyin
Chen, Haibo
Towey, Dave
Xia, Xin
A survey on adaptive random testing
title A survey on adaptive random testing
title_full A survey on adaptive random testing
title_fullStr A survey on adaptive random testing
title_full_unstemmed A survey on adaptive random testing
title_short A survey on adaptive random testing
title_sort survey on adaptive random testing
topic Adaptive random testing; random testing; survey
url https://eprints.nottingham.ac.uk/61352/
https://eprints.nottingham.ac.uk/61352/
https://eprints.nottingham.ac.uk/61352/