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Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from big data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing legacy information systems to enable integration with contemporary big data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for big data adoption. In addition, ameliorating uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing. Objective: We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in big data solution architecture and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The proposed approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with big data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders' preferences. The efficacy of our approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to big data architecture.

Service-orientation is a promising paradigm that enables the engineering of largescale distributed software systems using rigorous software development processes. The existing problem is that every service-oriented software development project often requires a customized development process that provides specific service-oriented software engineering tasks in support of requirements unique to that project. To resolve this problem and allow situational method engineering, we have defined a set of method fragments in support of the engineering of the project-specific service-oriented software development processes. We have derived the proposed method fragments from the recurring features of eleven prominent service-oriented software development methodologies using a systematic mining approach. We have added these new fragments to the repository of OPEN Process Framework to make them available to software engineers as reusable fragments using this well-known method repository.

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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Cloud computing literature provides a variety of perspectives towards the migration process, each with a different focus and mostly adopting heterogeneous technical-centric terminologies. Little, if any, studies have focused on developing an integrated and abstract process models which captures core domain constructs relevant to the cloud migration. By applying the metamodeling theoretical foundation, this article develops a generic process metamodel, as a domain language, for cloud migration. The metamodel is evaluated and refined through a three step approach including three case studies, domain expert review, and prototype system test. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration processes that suit given migration scenarios.