Software

Machine learning (ML) engineering increasingly incorporates principles from software and requirements engineering to improve development rigor; however, key non-functional requirements (NFRs) such as interpretability and explainability remain difficult to specify and verify using traditional requirements practices. Although prior work defines these qualities conceptually, their lack of measurable criteria prevents systematic verification. This paper presents a novel provenance-driven approach that decomposes ML interpretability and explainability NFRs into verifiable functional requirements (FRs) by leveraging model and data provenance to make model behavior transparent. The approach identifies the specific provenance artifacts required to validate each FR and demonstrates how their verification collectively establishes compliance with interpretability and explainability NFRs. The results show that ML provenance can operationalize otherwise abstract NFRs, transforming interpretability and explainability into quantifiable, testable properties and enabling more rigorous, requirements-based ML engineering.

The increasing decentralization of industrial processes in Industry 4.0 necessitates the distribution and coordination of resources such as machines, materials, expertise, and knowledge across organizations in a value chain. To facilitate effective operations in such distributed environments, it is essential to digitize processes and resources, establish interconnectedness, and implement a scalable management approach. The present paper addresses these challenges through the knowledge-based production planning (KPP) system, which was originally developed as a monolithic prototype. It is argued that the KPP-System must evolve towards a service-oriented architecture (SOA) in order to align with distributed and interoperable Industry 4.0 requirements. The paper provides a comprehensive overview of the motivation and background of KPP, identifies the key research questions that are to be addressed, and presents a conceptual design for transitioning KPP into an SOA. The approach under discussion is notable for its consideration of compatibility with the Arrowhead Framework (AF), a consideration that is intended to ensure interoperability with smart production environments. The contribution of this work is the first architectural concept that demonstrates how KPP components can be encapsulated as services and integrated into local cloud environments, thus laying the foundation for adaptive, ontology-based process planning in distributed manufacturing. In addition to the conceptual architecture, the first implementation phase has been conducted to validate the proposed approach. This includes the realization and evaluation of the mediator-based service layer, which operationalizes the transformation of planning data into semantic function blocks (FBs) and enables the interaction of distributed services within the envisioned SO-KPP architecture. The implementation demonstrates the feasibility of the service-oriented transformation and provides a functional proof of concept for ontology-based integration in future adaptive production planning systems.

The stream pipeline idiom provides a fluent and composable way to express computations over collections. It gained widespread popularity after its introduction in .NET in 2005, later influencing many platforms, including Java in 2014 with the introduction of Java Streams, and continues to be adopted in contemporary languages such as Kotlin. However, the set of operations available in standard libraries is limited, and developers often need to introduce operations that are not provided out of the box. Two options typically arise: implementing custom operations using the standard API or adopting a third-party collections library that offers a richer suite of operations. In this article, we show that both approaches may incur performance overhead, and that the former can also suffer from verbosity and reduced readability. We propose an alternative approach that remains faithful to the stream-pipeline pattern: developers implement the unit operations of the pipeline from scratch using a functional yield-based traversal pattern. We demonstrate that this approach requires low programming effort, eliminates the performance overheads of existing alternatives, and preserves the key qualities of a stream pipeline. Our experimental results show up to a 3× speedup over the use of native yield in custom extensions.