ÌâÄ¿£ºAdaptive Random Testing for Identification and Construction of Failure Regions

Ö÷½²£ºRubing Huang½ÌÊÚ

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Rubing Huang received the Ph.D. degree in computer science and technology from the Huazhong University of Science and Technology, Wuhan, China, in 2013. From 2016 to 2018, he was a visiting scholar at Swinburne University of Technology and at Monash University, Australia. He is an associate professor in the School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology (MUST). Before joining MUST, he worked as an associate professor at Jiangsu University, China. His current research interests include AI for software engineering, software engineering for AI, software testing, debugging, and maintenance.

He has more than 60 publications in journals and proceedings, including in IEEE Transactions on Software Engineering, IEEE Transactions on Reliability, Journal of Systems and Software, Information and Software Technology, Software: Practice and Experience, Science of Computer Programming, IET Software, International Journal of Software Engineering and Knowledge Engineering, IEEE Internet of Things Journal, Information Sciences, The Computer Journal, Security and Communication Networks, ICSE, ISSRE, ICST, and COMPSAC. His research has been supported by the National Natural Science Foundation of China (including the general and youth programs), the Science and Technology Development Fund of Macau (the general program), and the China Postdoctoral Science Foundation (including the special grade and the general program). He is a senior member of the IEEE and the China Computer Federation. More information about him and his work is available online at https://huangrubing.github.io/.

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As an enhancement of Random Testing (RT), Adaptive Random Testing (ART) has been proposed to guarantee the diversity of test cases, based on the observation that neighboring inputs normally exhibit similar failure behavior. Many ART approaches have been investigated, according to different notations. In this report, we are going to present several approaches of ART, and provide some new progress of our research group. In addition, we also discuss how to adopt ART for constructing failure regions, after receiving a failure-causing input.