一句话：从记录的事件中自动挖掘事件发生、时序与数据约束，以多种排序呈现给用户，支撑系统之系统的运行时监控。
系统之系统（systems of systems）的行为往往只有在运行期、各子系统及其环境相互交互时才充分涌现，因此需要运行时监控来检测偏离预期的行为；但预期行为通常由工程师用时序逻辑或领域特定语言手工定义，而在多团队独立开发异构组件的系统之系统中，这种定义所需的深度领域知识常常在开发期并不具备。本文介绍作者正在进行的博士研究：从记录的事件中自动挖掘约束，所挖掘的约束涵盖事件的发生、时间、数据以及这些属性的组合；该方法进一步以多种排序策略将挖掘结果呈现给用户，并可用于支持系统演化场景。摘要未报告具体的评估方法与实证结果。
The behavior of complex software-intensive systems of systems often only fully emerges during operation, when all systems interact with each other and with their environment. Runtime monitoring approaches are thus used to detect deviations from the expected behavior, which is commonly defined by engineers, e.g., using temporal logic or domain-specific languages. However, the deep domain knowledge required to specify constraints is often not available during the development of systems of systems with multiple teams independently working on heterogeneous components. In this paper, we thus describe our ongoing PhD research to automatically mine constraints for runtime monitoring from recorded events. Our approach mines constraints on event occurrence, timing, data, and combinations of these properties. The approach further presents the mined constraints to users offering multiple ranking strategies and can also be used to support users in system evolution scenarios.
DOI 原文 ·
@inproceedings{paperbot4707,
title = {Automatic mining of constraints for monitoring systems of systems},
author = {Thomas Krismayer},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3241532}
}