一句话：提出 MoD2 检测器，融合参数偏差估计、不确定性补偿与安全区域量化，及时准确检测自适应系统中的模型偏差。
基于控制的自适应软件密集型系统（SAS）因具有最优性能和自适应行为可靠性的理论保证而受关注，但运行时模型偏差常破坏这些保证。本文提出模型引导的偏差检测器 MoD2，用于及时、准确地检测模型偏差。MoD2 将参数偏差估计、不确定性补偿和安全区域量化融合起来，以兼顾及时性与准确性。为确保可靠性，SAS 在检测到不安全的模型偏差后，可从基于控制的最优控制器切换到强制控制器。作者用三个示例系统进行实证评估，结果显示 MoD2 将检测延迟缩短 93.3%，假阴性率降低 39.4%，假阳性率降低 25.2%；自适应切换机制使异常率下降 29.2%。
Control-based approaches to self-adaptive software-intensive systems (SASs) are hailed for their optimal performance and theoretical guarantees on the reliability of adaptation behavior. However, in practice the guarantees are often threatened by model deviations occurred at runtime. In this paper, we propose a Model-guided Deviation Detector (MoD2) for timely and accurate detection of model deviations. To ensure reliability, a SAS can switch a control-based optimal controller for a mandatory controller once an unsafe model deviation is detected. MoD2 achieves both high timeliness and high accuracy through a deliberate fusion of parameter deviation estimation, uncertainty compensation, and safe region quantification. Empirical evaluation with three exemplar systems validated the efficacy of MoD2 (93.3% shorter detection delay, 39.4% lower FN rate, and 25.2% lower FP rate), as well as the benefits of the adaptation-switching mechanism (abnormal rate dropped by 29.2%).
@inproceedings{paperbot6408,
title = {Timely and accurate detection of model deviation in self-adaptive software-intensive systems},
author = {Yanxiang Tong and Yi Qin and Yanyan Jiang and Chang Xu and Chun Cao and Xiaoxing Ma},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468548}
}