@inproceedings{52f9e31e4d884234b1c23ad288f42719,
title = "Research on Test Cases Screening Method Based on Generative Adversarial Networks",
abstract = "In order to overcome the limitations of traditional fuzzy testing methods in generating diversified and high-coverage test cases, this paper adopts the technique of generative adversarial networks (GANs) and proposes a test case screening method based on an improved Information Maximizing Generative Adversarial Network (infoGAN). To improve the efficiency and effectiveness of fuzzy testing of industrial robotic systems, the adversarial training of generators and discriminators is optimized, enabling the improved infoGAN model to generate high-quality and diverse test cases. The test results indicate that the test cases screened by this method significantly enhance the coverage and effectiveness of vulnerability detection in industrial robots and improve the overall impact of fuzzy testing.",
keywords = "Fuzzy testing, Generative Adversarial Networks (GANs), Industrial robots, Information Maximizing Generative Adversarial Network (infoGAN), Test cases",
author = "Zhongwei Li and Ji Sun and Xianji Jin and Zihan Ma",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024 ; Conference date: 21-06-2024 Through 23-06-2024",
year = "2024",
month = oct,
day = "24",
doi = "10.1145/3690407.3690600",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
pages = "1165--1169",
booktitle = "Proceedings of 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024",
address = "美国",
}