摘要
自适应系统由于其能够自主地适应具有非确定性的部署环境,并持续地保持用户的满意度,受到了广泛的关注.然而,目前仍然存在未解决的挑战,例如如何在新的部署环境下,或者在开放且复杂的环境下,使得系统仍然能满足自适应性.因此,为自适应系统的设计引入了一个新的概念模型,受归因理论启发,该模型被设计成内归因和外归因两层结构.内归因层决定了内因如何影响自适应行为,这一层与部署环境解耦,可以独立设计且可以复用在不同的部署环境中.外归因层映射了外因与内因的关系,这一层在不同的部署环境中可以被替换.基于两层结构的实现框架,具有设计且实现自适应系统的适用性,以及内因层适应逻辑的可复用性,通过两个案例,一个是被广泛使用的电子商务网络应用,一个是需要躲避障碍物且避免滑倒和翻转的机器人系统,来进行评估.
The development of self-adaptive systems has attracted much attention as they can adapt themselves autonomously to environmental dynamics and maintain user satisfaction.However,there are still tremendous challenges remained.One major challenge is to guarantee the reusability of the system and extend the adaptability with changing deployment environments,or open and complex environments with the existence of unknown.To solve these problems,a conceptual self-adaptive model is introduced,decoupling the environment with the system.This model is a two-layer structure based on internal causes and external causes from the attribution theory.The first layer,determining how the internal causes affect the adaptation behaviors,is independently designed and reusable whiles the second layer,mapping the relationship between external causes with internal causes,is replaceable and dynamically bound to different deployment environments.The proposed approach is evaluated by two case studies,a widely used benchmark e-commerce Web application and a destination-oriented robot system with obstacle and turnover avoidance,to demonstrate its applicability and reusability.
作者
李念语
陈正胤
刘坤
焦文品
LI Nian-Yu;CHEN Zheng-Yin;LIU Kun;JIAO Wen-Pin(Department of Computer Science and Technology,School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China;Key Laboratory of High Confidence Software Technologies of Ministry of Education(Peking University),Beijing 100871,China)
出处
《软件学报》
EI
CSCD
北大核心
2021年第7期1957-1977,共21页
Journal of Software
基金
国家重点基础研究发展计划(973)(2016YFB000105,2015CB352200)
国家自然科学基金(61620106007)。
关键词
自适应软件系统
归因理论
可复用性
环境非确定性
self-adaptive software
attribution theory
reusability
uncertainty of environment