Intro
Preface
Contents
Challenges and Opportunities in Quantum Software Architecture
Introduction
Background
Overview
RQ1: Do We Need to Consider QSA Designs in a Specialized Way?
Accessing Quantum Computers Through Cloud
Use of Existing Notations via Extensions
Need for Quantum-Specific Constraints
Classifying Quantum-Specific Quality Attributes
Prevalence of Low-Level Design Choices in Architectural Designs
RQ2: What Are Relevant Architecture-Level Concerns for QC?
Integrating Quantum Components into Existing Architectures

Concerning Hardware-Specific Properties in Architectural Designs
Hardware and Software Coupling
Hardware-Specific Optimization
Consequences
Other Considerations
RQ3: Is Knowledge Gained from the Classical World Still Applicable to the Quantum World, and to Which Extent?
Standards
Architecture Description Languages
Architectural Patterns
Quantum DevOps and Quantum Services
RQ4: How to Run Hybrid Applications from an Architectural Perspective?
Conclusion
References
Software Architectures for AI Systems: State of Practiceand Challenges
Introduction

State of Practice
Key Roles and Activities
Conceptual Architecture and the Overall Process for Developing AI Systems
Example: Development of a Recommender System
Challenges
Research Directions
Conclusion
References
Architecting and Engineering Value-Based Ecosystems
Introduction
State of the Art
Engineering of Value-Based Systems
Application to Value-Based Systems
Problem Description
Motivating Scenarios
The Problem and Related Challenges
Value-Based Ecosystems
Aspects and Architectural Implications of Solution Approaches

Human- and Values-Centered Systems
Dealing with Fuzziness
Dealing with Uncertainty
Quality of Data/Bias Removal
Ensuring Values by Design
Verification and Validation
Value-Based Supervisor
Governance Mechanisms
Reputation and Incentives
Usage of Digital Twins
Research Opportunities
Summary
References
Continuous Alignment Between Software Architecture Design and Development in CI/CD Pipelines
Introduction and Motivation
Background and State of the Art
Motivational Examples
Practical Experience at Google
Practical Experience in Various Domains

Vision and Proposed Solution
Summary and Outlook
References
An Empirical Basis for Software Architecture Research
Introduction
Motivation
Artifacts
Related Work
Mining Software Repositories
Mining Software Repositories for Software Architecture
Pattern Mining
API Evolution Analytics
Public Repositories for Datasets
Problems
Opportunities
Summary and Outlook
References
A Better Way to Teach Software Architecture
Introduction
Teaching Resources
Industry Talent Needs and Architecture Realities
Duties, Skills, and Knowledge
Knowledge