Journal of Physics: Conference Series
Artificial intelligence applications provide tremendous opportunities to improve human life and drive innovation. AI systems/applications which operate in a real-world environment have to encounter an infinite set of feasible scenarios. Conventional testing approach to test the AI application allows only limited testing and does not allow taking the different contexts into consideration and may lead to insufficient validation and characterization. Therefore, to ensure robustness, certainty and reliability of AI applications, the authors applied classification-based AI software testing framework and 3D decision tables to generate test cases. Moreover, the authors compared the quality assurance metrics (accuracy, correctness, reliability and consistency) of AI and non-AI functions in the AI mobile application scenario. Our results indicate and confirm that complete AI function validation is not possible with conventional testing methods, but AI software testing strategy proposed based on classification framework and 3D decision tables has a good effect.
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Chi Thi Phuong Tran, Mohana Gudur Valmiki, Guoyan Xu, and Jerry Zeyu Gao. "An intelligent mobile application testing experience report" Journal of Physics: Conference Series (2021). https://doi.org/10.1088/1742-6596/1828/1/012080