ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (9): 1929-1946.doi: 10.7544/issn1000-1239.20220015

• 软件技术 • 上一篇    下一篇



  1. (计算机软件新技术国家重点实验室(南京大学) 南京 210023) (
  • 出版日期: 2022-09-01
  • 基金资助: 

Evaluating the Fitness of Model Deviation Detection Approaches on Self-Adaptive Software Systems

Tong Yanxiang, Qin Yi, Ma Xiaoxing   

  1. (State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023)
  • Online: 2022-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62025202, 61932021, 61902173) and the Natural Science Foundation of Jiangsu Province (BK20190299).

摘要: 自适应软件系统的模型偏差会导致诸多可靠性问题.对控制型自适应软件系统而言,其面临的模型偏差源自描述软件系统的标称模型在非确定运行环境中的漂移现象.现有模型偏差检测方法往往忽视了不同模型偏差之间的差异性,导致用户难以为其特定的应用场景选择合适的检测方法.针对这一问题,提出了一套特性指标,用于评估模型偏差检测方法在不同模型偏差场景下的适用性.该特性指标基于提出的模型偏差检测框架,系统分析了模型偏差检测过程中的重要因素,并提取控制信号强度、环境输入强度和非确定性强度作为量化的特性指标.基于这些特性指标,实验研究4种主流模型偏差检测方法在不同场景下的检测效果,并总结不同模型偏差检测方法对于自适应软件系统不同特性场景的适用性.

关键词: 自适应软件, 控制型自适应软件系统, 模型偏差, 运行时验证, 场景特性

Abstract: Model deviations in self-adaptive software systems cause critical reliability issues. For control-based self-adaptive systems, model deviation roots in the drifting of the managed system’s nominal model in uncertain running environments, which causes the invalidation of provided formal guarantees, and may lead to system’s abnormal behavior. Existing model deviation detection approaches often ignore the characteristics of model deviations that emerge in different scenarios. This makes it difficult for users to choose an appropriate approach in a specific application scenario. We provide a framework to describe different detection approaches and propose three metrics to evaluate a detection approach’s fitness with respect to different types of model deviations. The provided framework is composed of four parts, namely system modelling, detection variable estimation, model deviation representation, and model deviation judgement, based on the process of model deviation detection. The proposed metrics, including control-signal-intensity, environmental-input-intensity, and uncertainty-intensity, concern three key factors in the process of model deviation detection, respectively. Using these metrics, a deviation scenario is quantified with a vector and is classified by the quantified values into a characteristic scenario according to control theory. A number of experiments are conducted to study the effectiveness of four mainstream model detection approaches in different scenarios, and their fitness to different characteristic scenarios of model deviations is summarized.

Key words: self-adaptive software, control-based self-adaptive systems, model deviation, runtime verification, scenario characteristics