Large language models (LLMs) have enabled new tools in requirements engineering (RE), often in the form of intelligent agents or virtual assistants. These tools can transform how software engineers perform RE tasks and interact with stakeholders. However, existing research primarily focuses on showcasing the capabilities of these tools rather than their design and evaluation in RE-specific contexts. This limits our understanding of their practical value and hinders broader adoption. To address this gap, we propose a reference model to guide the design, use, and evaluation of intelligent RE agents. Our work introduces new RE use cases, along with evaluation metrics for intelligent RE agents. We present a study design to support systematic development and share early findings demonstrating the feasibility of our approach. The use cases show how agents can add value for RE practitioners, while our synthesized catalogue supports tool evaluation. Finally, our analysis of commercial agents reveals that these tools already support certain aspects of the envisioned RE use cases.

Kindness is a psycho-social phenomenon that is also recognized as an important pro-social behaviour. The use of digital technology provides opportunities to promote kindness in various ways, such as in social media campaigns and online communities. In principle, software engineers are well positioned to develop automated systems that can facilitate software-mediated kindness. However, in practice, incorporating kindness concerns explicitly in the development and use of software systems is challenging: kindness is highly context dependent, affected by a range of factors such as intentions and opportunity.

Supply chain fraud involving counterfeit or adulterated products presents threats to human health and safety. Quality inspection is a key fraud mitigation tool where inspection planning involves allocating inspection resources across geographically dispersed assets considering both the cost and value of the inspection. I4.0 environments pose further challenges as their heterogeneous and dynamic cyber-physical environment creates a large inspection resource allocation solution space, causing the corresponding analysis to be computationally complex. In this article, we contribute to supporting optimal inspection decisions of dynamic cyber-physical supply chains through the use of structural representations—topologiesof the supply chain, physical premises, and their production context. We present an approach for topology modeling of supply chains and illustrate its use within an adaptive inspection approach, showing that structural information can reduce malicious process discovery times by up to 90%.

Cyber-physical systems (CPSs) are part of many critical infrastructures such as industrial automation and transportation systems. Thus, security incidents targeting CPSs can have disruptive consequences to assets and people. As incidents tend to re-occur, sharing knowledge about these incidents can help organizations be more prepared to prevent, mitigate or investigate future incidents. This paper proposes a novel approach to enable representation and sharing of knowledge about CPS incidents across different organizations. To support sharing, we represent incident knowledge (incident patterns) capturing incident characteristics that can manifest again, such as incident activities or vulnerabilities exploited by offenders. Incident patterns are a more abstract representation of specific incident instances and, thus, are general enough to be applicable to various systems - different from the one in which the incident originally occurred. They can also avoid disclosing potentially sensitive information about an organization's assets and resources. We provide an automated technique toextractan incident pattern from a specific incident instance. To understand how an incident pattern can manifest again in other cyber-physical systems, we also provide an automated technique toinstantiateincident patterns to specific systems. We demonstrate the feasibility of our approach in the application domain of smart buildings. We evaluate correctness, scalability, and performance using two substantive scenarios inspired by real-world systems and incidents.

The effective functioning of society is increasingly reliant on supply chains which are susceptible to fraud, such as the distribution of adulterated products. Inspection is a key tool for mitigating fraud, however it has traditionally been constrained by physical characteristics of supply chains such as their size and geographical distribution. The increasingly cyber-physical nature of supply chains, their autonomy, and their data richness, extends their attack surfaces and thus increases opportunities for fraud. However, it also presents new opportunities for increased and dynamic inspection, which in turn requires more targeted and flexible inspection regimes. In this paper we explore opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. Through using structural representations of supply chains (topological models) we propose defining optimal inspection zones. Such zones circumscribe assets of interest to optimise observation while reducing the intrusiveness of inspection. Using a motivating example of adulterated pharmaceuticals and a proof-of-concept tool we illustrate adaptive inspection, and surface challenges to its realisation, such as value metrics, forensic readiness integration and managing contrasting local and global perspectives.