2016
DOI: 10.1109/tse.2015.2478001
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Metamorphic Testing for Software Quality Assessment: A Study of Search Engines

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Cited by 136 publications
(90 citation statements)
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References 29 publications
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“…Their results showed that metamorphic testing effectively detected inconsistencies in the searches in terms of both returned content and ranking quality. In later work [90], the authors performed an extensive empirical study on the web search engines Google, Bing, Chinese Bing and Baidu. As a novel contribution, metamorphic relations were defined from the user perspective, representing the properties that a user expects from a "good" search engine, regardless of how the engine is designed.…”
Section: Web Services and Applicationsmentioning
confidence: 99%
“…Their results showed that metamorphic testing effectively detected inconsistencies in the searches in terms of both returned content and ranking quality. In later work [90], the authors performed an extensive empirical study on the web search engines Google, Bing, Chinese Bing and Baidu. As a novel contribution, metamorphic relations were defined from the user perspective, representing the properties that a user expects from a "good" search engine, regardless of how the engine is designed.…”
Section: Web Services and Applicationsmentioning
confidence: 99%
“…However, In RELAI, the reliability is not considered and development in relation between uncertainties associated with operational profile also remained unsolved. Based on two contributions, metamorphic testing is extended into user-oriented approach is used in [3], to attain software verification, validation, and quality assessment. In the first contribution, oracle and lack of specification problem is solved through validation, software verification and quality assessment.…”
Section: Literature Surveymentioning
confidence: 99%
“…Metamorphic testing is the technique for addressing the oracle problem though developing them with MRs [2] [16]. It has been applied to several domains such as bioinformatics systems, machine learning systems, and online service systems [10] [11] [20]. Murphy et al classified six types of MRs for testing machine-learning systems [15], which are useful for creating initial MRs. Xie et al investigated some specific MRs that were extended from general MRs for testing machine learning applications [18].…”
Section: Related Workmentioning
confidence: 99%