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Author Yoo Jin Lim ♦ Eunkyoung Jee ♦ Donghwan Shin ♦ Doo-Hwan Bae
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Cameras ♦ Testing ♦ Adaptive systems ♦ Monitoring ♦ Organizations ♦ Context ♦ Scalability ♦ agent-based adaptive system ♦ software quality attributes ♦ system testing ♦ collective adaptive system
Abstract Collective adaptive systems (CAS) consist of multiple agents that adapt to changing system and environmental conditions in order to satisfy system goals and quality requirements. As more applications involve using CAS in a critical context, ensuring the correct and safe adaptive behaviors of quality-driven CAS has become more important. In this paper, we propose Collective Adaptive System Testing (CAST), a scalable and efficient approach to testing self-adaptive behaviors of CAS. We propose a selective method to instantiate and execute test cases relevant to the current adaptation context. This enables testers to focus testing on key self-adaptive behaviors while dealing with the scale and dynamicity of the system. An experimental evaluation using a traffic monitoring system is performed to validate its scalability, efficiency, and fault-detection effectiveness. The experimental results provide insights into how CAST can serve as a feasible and effective assurance technique for CAS.
Description Author affiliation: Sch. of Comput., KAIST, Daejeon, South Korea (Eunkyoung Jee; Donghwan Shin; Doo-Hwan Bae) || Server Technol. Div., Oracle Korea, Seoul, South Korea (Yoo Jin Lim)
ISSN 07303157
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-07-01
Publisher Place Taiwan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781467365642
Size (in Bytes) 438.38 kB
Page Count 6
Starting Page 216
Ending Page 221


Source: IEEE Xplore Digital Library