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Due to its enormous benefits, the research and industry communities have shown an increasing interest in the Microservices Architecture (MSA) style over the last few years. Despite this, there is a limited evidence-based and thorough understanding of the types of issues (e.g., faults, errors, failures, mistakes) faced by microservices system developers and causes that trigger the issues. Such evidence-based understanding of issues and causes is vital for longterm, impactful, and quality research and practice in the MSA style. To that end, we conducted an empirical study on 1,345 issue discussions extracted from five open source microservices systems hosted on GitHub. Our analysis led to the first of its kind taxonomy of the types of issues in open source microservices systems, informing that the problems originating from Technical debt (321, 23.86%), Build (145, 10.78%), Security (137, 10.18%), and Service execution and communication (119, 8.84%) are prominent. We identified that "General programming errors", "Poor security management", "Invalid configuration and communication", and "Legacy versions, compatibility and dependency" are the predominant causes for the leading four issue categories. Study results streamline a taxonomy of issues, their mapping with underlying causes, and present empirical findings that could facilitate research and development on emerging and next-generation microservices systems. CCS CONCEPTS• Software and its engineering → Designing software; • General and reference → Empirical studies.

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.

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Quantum Software Engineering (QSE) is a recent trend -focused on unifying the principles of quantum mechanics and practices of software engineering -to design, develop, validate, and evolve quantum age software systems and applications. Software architecture for quantum computing (a.k.a. quantum software architectures (QSA)) supports the design, development, and maintenance etc. phases of quantum software systems using architectural components and connectors. QSA can enable quantum software designers and developers to map the operations of Qubits to architectural components and connectors for implementing quantum software. This research aims to explore the role of QSAs by investigating (i) architectural process having architecting activities, and (ii) human roles that can exploit available tools to automate and customise architecturecentric implementation of quantum software. Results of this research can facilitate knowledge transfer, enabling researchers and practitioners, to address challenges of architecture-centric implementation of quantum software systems.

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