@inproceedings{7798692534614dcc91d0566288a6f3bf,
title = "Exploring metamorphic testing for fake-news detection software: a case study",
abstract = "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 applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.",
keywords = "fake news, fake news detection, fake news detection software, metamorphic relation, Metamorphic testing, oracle problem, software testing",
author = "Lin Miao and Dave Towey and Yingrui Ma and Chen, {Tsong Yueh} and {Quan Zhou}, Zhi",
note = "Funding Information: The authors acknowledgethe financial support from the Artificial Intelligence and Optimisation (AIOP) research group, the Faculty of Science and Engineering (FoSE), the International Doctoral Innovation Centre, Ningbo Education Bureau, Ningbo Science and Technology Bureau, and the University of Nottingham. We would like to thank Alexander Kidd of FactoidL27, Ulrich Schade from Fraunhofer28, the RapidAPI29 customer service team, the technical team at Paraphrase Genius, and Dhruv Ghulati and Ying Tang from Content Score. Publisher Copyright: {\textcopyright} 2023 IEEE.; 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 ; Conference date: 26-06-2023 Through 30-06-2023",
year = "2023",
doi = "10.1109/COMPSAC57700.2023.00122",
language = "English",
series = "Proceedings - International Computer Software and Applications Conference",
publisher = "IEEE Computer Society",
pages = "912--923",
editor = "Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and Majumder, {AKM Jahangir Alam} and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Ahamed, {Sheikh Iqbal}",
booktitle = "Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023",
address = "United States",
}