论文标题
旨在使用概率模型来设计具有固有不确定性的软件系统
Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty
论文作者
论文摘要
软件系统中机器学习（ML）组件的采用引起了新的工程挑战。特别是，有关功能适用性和操作环境的固有不确定性使建筑评估和权衡分析变得困难。我们提出了一种软件体系结构评估方法，称为设计过程中的建模不确定性（MUDD），该方法明确对与ML组件相关的不确定性进行了建模，并评估了它如何通过系统传播。该方法支持有关架构模式如何减轻不确定性的推理，并可以比较针对ML和经典软件组件之间相互作用的不同体系结构。虽然我们的方法是域 - 不可思议的，并且适合任何不确定性起着核心作用的系统，但我们以例如自主驾驶的感知系统来证明我们的方法。
The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.
