摘要:目前自适应软件正在为众多领域系统提供着对运行环境的适应能力.如何建立一种能够保障识别质量的自适应分析方法，使之可从运行环境中快速且准确地识别出异常事件，是确保自适应软件长期稳定运行所必须考虑的研究问题之一.当前运行环境的不确定性给该问题的攻关带来两方面的挑战：其一，现有分析方法一般通过预先建立环境状态与事件之间的映射关系来识别事件.但在系统运行之前，已无法仅凭经验确定环境状态并建立全面且正确的映射关系.仅依赖映射关系建立分析方法的设计思路已无法保障识别的准确性.其二，不确定环境何时会发生何种事件已变得不可预期.如果采用现有设计思路，定期地获取环境状态再进行事件识别，则无法保障识别效率.然而，目前却缺乏应对这些紧迫挑战的相关工作，因此提出了一种基于事件关系保障识别质量的自适应分析方法（self-adaptation analysis method for recognition quality assurance using event relationships，简称SAFER）.SAFER采用序列模式挖掘算法、模糊故障树与贝叶斯网络等技术抽取并建模事件因果关系，并基于该类关系与映射关系通过贝叶斯网络的正向推理能力共同识别事件，与传统的仅依赖映射关系的识别方法相比可保证识别的准确性；基于贝叶斯网络的反向推理能力，确定易引发事件的精英感知对象，并动态调整获取精英感知对象状态数据的采样周期，以便于在事件发生后尽快获得相关环境状态，从而保障识别效率.实验结果表明，在自适应软件实际运行过程中，SAFER可实现对事件的识别并保障识别准确性与识别效率，为自适应软件稳定运行提供了有效支持.



Abstract:At present, self-adaptive software is providing the ability to adapt to the operating environment for many systems in different fields.How to establish a self-adaptation analysis method which can recognize abnormal events at runtime quickly and achieve the recognition quality assurance, is one of the research issues that must be considered to ensure the long-term stable operation of the self-adaptive software. The uncertainty of the runtime environment brings two challenges to this problem. On the one hand, the analysis method usually recognizes the events by pre-establishing the mapping relationships between the environment state and the events. However, due to the complexity of the operating environment and the unknown changes, it is impossible to establish comprehensive and correct mapping relationships based on experience before the system is running, which affect the accuracy of event recognition; On the other hand, the changing operating environment makes it impossible to accurately predict when and which event will occur. If the current way is used to obtain the environmental status using constant sensing period and recognize events, then the recognition efficiency cannot be guaranteed. However, it is still blank about how to deal with these urgent challenges. Therefore, this study proposes a self-adaptation analysis method for recognition of quality assurance using event relationships (SAFER). SAFER uses sequential pattern mining algorithm, fuzzy fault tree (FFT), and Bayesian network (BN) to extract and model the causalities between events. This study uses the event causal relationships and mapping relationships to recognize events through the BN forward reasoning, which can ensure the accuracy of recognition compared with the traditional analysis methods that only rely on mapping relationships. Moreover, this study establishes the elitist set of monitoring objects through the BN backward reasoning, then modifies the sensing period of monitoring objects in elitist set dynamically in order to obtain the environmental status as soon as possible after the abnormal events occurred, so as to ensure the efficiency of recognition. The experimental results show that SAFER can effectively improve the accuracy and efficiency of the analysis process, and support long-term stable operation of self-adaptive software.



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