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Exploring Metamorphic Testing for Fake-News Detection Software: A Case by Lin Miao, Dave Towey et al

Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software's output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicab

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