Write a Blog >>
ECSA 2020
Mon 14 - Fri 18 September 2020 L'Aquila, Italy

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 is based on Bayesian networks, which enable both qualitative and quantitative assessments of software architectures. In particular, 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 domain-agnostic and suitable for any system where uncertainty plays a central role, we validate our approach using as example a perception system for autonomous driving. For this system we empirically demonstrate that a component-based design is over 10% more resilient to uncertainty than an end-to-end design. Moreover, we bring empirically evidence that architecture design patterns can help to significantly decrease the uncertainty associated to ML components.

Fri 18 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

16:50 - 17:30
S13: Self-adaptation and Uncertainity (II)Research Papers at ECSA 2020 Teams Channel
Chair(s): Xabier Larrucea Tecnalia, Gabriel A. Moreno Carnegie Mellon University

Virtualization support: Claudio Di Sipio

A Multi-Objective Performance Optimization Approach for Self-Adaptive Architecturesshort-paperResearch Track
Research Papers
Davide Arcelli Università degli Studi dell'Aquila
Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertaintyshort-paperResearch Track
Research Papers
Alex Serban Radboud University, Erik Poll Radboud University Nijmegen, Joost Visser Leiden University