The rapid digital transformation of manufacturing, often referred to as Industry 4.0, relies on seamless interoperability between physical and software assets. A central enabler is the Asset Administration Shell (AAS), a standardized digital representation of such assets. Recent advances in large language models (LLMs) enable the generation of AAS submodels from unstructured sources such as product datasheets but raise challenges for quality assurance. In particular, unexpected errors, the lack of ground-truth references, and the absence of standardized quality metrics hinder reliable adoption. In this work, we evaluate quality metrics for AI-generated AAS using a perturbation-based evaluation framework. By systematically degrading AAS generation along multiple dimensions, we assess how well different metrics reflect quality changes. Based on a dataset of 200 products from multiple manufacturers, we generate 6,400 AAS instances using GPT-4o-mini, Qwen3, and DeepSeek-R1. Our results show that metrics based on exact matching of property names and similarity-based soft matching of property values, in particular value-based recall and name-based F1 score, provide the most reliable indicators of quality degradation. Furthermore, we quantify the impact of different perturbation types and analyze differences across model families and product segments. These findings support the selection of suitable metrics, the tuning of LLM-based pipelines, and the integration of AI-generated AAS into industrial applications.
A Cryptography Bill of Materials (CBOM) is a machine-readable inventory of cryptographic components—e.g., algorithms, keys, and certificates—that enables practitioners to assess risks ranging from classical cryptographic failures to post-quantum threats. However, despite growing regulatory and security pressure to operationalize cryptographic governance, there is little empirical evidence on the available open-source CBOM tools. To address this gap, we conducted a mining study of GitHub repositories that host CBOM tools: generation tools that extract cryptographic components from software artifacts to produce CBOMs, and consumption tools that analyze generated CBOMs to derive actionable insights. Our results reveal a generation–consumption disparity: 46 repositories host generation tools, while only seven host consumption tools. Community engagement appears limited: 93.48
InnerSource (IS) is increasingly viewed as a way to improve large-scale software development. However, evidence on its adoption in public sector organizations remains limited. This paper reports a study of IS adoption in a Swedish Public Sector Organization. Drawing on nine semi-structured interviews and an internal workshop with 13 participants, we examine motivations, challenges, and improvement strategies for IS adoption. The results show that the main motivations are improving reuse, strengthening cross-team collaboration, and enabling faster and more cost-efficient development. Key challenges relate to a lack of an open culture and mindset, management of reusable assets (e.g., ownership, documentation, and code visibility), and ways of working (e.g., siloed practices and limited time for contributions). The study also identified improvements to address challenges, including fostering an enabling culture and mindset for IS and knowledge sharing, strengthening management support, and establishing internal platforms and governance for reusable assets.
Smart homes are increasingly common in everyday life, offering convenience, automation, energy efficiency, and enhanced security through software services and devices such as smart locks, cameras, and voice assistants. However, their connected and shared nature introduces risks within close relationships, where they can be exploited to facilitate interpersonal violence and abuse. At the same time, they can also be leveraged for mitigation, detection, and victim support. In this study, we conduct a systematic mapping review of 27 studies to identify and categorize how smart home systems can both facilitate and address interpersonal violence and abuse. The findings show that research in this field is imbalanced, with most studies focusing on abuse facilitation, particularly through surveillance, remote control, and access misuse, while fewer studies propose mechanisms for abuse detection and victim support. This suggests a need for more holistic, smart home system-integrated approaches that explicitly address interpersonal risks in smart home design and development, and integrate these concerns into core software engineering activities, including requirements engineering, system design, and validation.
Software development aims to deliver products that fulfill stakeholder goals. However, conflicting interests and uneven influence within an organization often produce fragmented or incomplete requirements. A structured and multiperspective analysis of stakeholder goals is therefore necessary to understand these divergences and to form a holistic view of the organizational landscape. This paper proposes a method that integrates the strengths of KAOS and iStar while maintaining compliance with IEEE 29148. By enabling the analysis of individual stakeholder perspectives alongside primary system goals aligned with organizational strategies, the method supports the derivation of valid requirements specifications. We illustrate the initial feasibility of this approach through a case study on Administrative Digital Transformation initiatives in Japan.
