<bold>IST A</bold> +: Test case generation and optimization for intelligent systems based on coverage analysis

被引:0
作者
Wu, Xiaoxue [1 ,4 ]
Gu, Yizeng [2 ]
Lin, Lidan [2 ]
Zheng, Wei [2 ]
Chen, Xiang [3 ]
机构
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China
[2] Northwestern Polytech Univ, Sch Software, Xian, Peoples R China
[3] Nantong Univ, Sch Informat Sci & Technol, Nantong, Peoples R China
[4] Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Minist Ind & Informat Technol, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning testing; Coverage criteria; Test case generation; Test case optimization;
D O I
10.1016/j.scico.2024.103078
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
With the increasing use of intelligent systems in various domains such as self -driving cars, robotics, and smart cities, it is crucial to ensure the quality of intelligent systems for their reliable and effective use in various domains. However, testing intelligent systems poses unique challenges due to their complex structure, low efficiency, and the high cost associated with manually collecting a large number of test cases. Hence, it is crucial to design tools that can adequately test intelligent systems while overcoming these obstacles. We propose an intelligent system test tool called ISTA+. This tool implements automatic generation and optimization of test cases based on coverage analysis, resulting in improved test adequacy for intelligent systems. To evaluate the effectiveness of ISTA+, we applied it to two different models (fully -connected DNN and the Rambo model) and two datasets of different data types (i.e., image and text). The evaluation results demonstrate that ISTA+ successfully improves the test dataset quality and ensures comprehensive testing for both text and image data types. center dot Link to source code: https://github .com /wuxiaoxue /ISTAplus center dot Link to video demonstration: https://youtu .be /6CkzMJ0ghq8
引用
收藏
页数:11
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