In safety-critical industries such as aerospace, managing uncertainty is important, both to ensure airworthiness and to control business risk. Although design margins are widely used to support safety, reliability and regulatory compliance, they are often applied conservatively, with rationales that are implicit, inconsistently documented or unevenly interpreted. While the use of margins is necessary from a safety, reliability and regulatory perspective, excessive use of margins can lead to undesirable effects such as overdesign, consequently leading to heavier parts and therefore design inefficiencies. While the role of margins in this context is well appreciated, there is a gap between the theoretical understanding of margins and their use in practice, especially in relation to uncertainty. This paper investigates this gap through a qualitative study involving 11 in-depth interviews with experienced engineers and managers at a leading aerospace component design and manufacturing company. The interviews explore the current industrial practices, cultural barriers and decision-making heuristics surrounding the practice of design margins. The findings reveal a reliance on legacy practices and tacit knowledge, which may limit margin transparency and potentially contribute to margin stacking. We conclude with actionable implications for the proper documentation and use of margins in engineering design.

Product development can be understood as a dynamic socio-technical ecosystem comprising interdependent elements such as products, people, processes, methods, tools, ICT environments and their contexts. This ecosystem perspective emphasizes the evolutionary and adaptive nature of product development environments, where change is not isolated but propagated through complex networks of actors, processes, tools and infrastructures. Influences from regulatory transformations, sustainability demands, and advances in digital technologies continuously reshape these ecosystems, requiring an integrated, systemic understanding to navigate and manage their evolution effectively. Within this framework, methods and tools are not static instruments but co-evolving entities embedded in a larger system of development practices. Their successful implementation depends on coherence with existing processes and compatibility with the knowledge and competencies of stakeholders. New tools often emerge from technological innovation—particularly in artificial intelligence and data analytics—and in turn reshape the methods that support development activities. When these changes are not aligned or are poorly integrated, they can generate disruptions analogous to ecological imbalances, leading to inefficiencies or resistance to adoption. The increasing complexity of products, including cyber-physical and product-service systems, demands interdisciplinary collaboration and context-specific integration of practices. Disciplines such as mechanical engineering, computer science, and service design bring distinct approaches and expectations, making harmonization of processes and terminology essential. Organizational challenges, such as varying digital competencies and generational shifts in work habits, further contribute to the dynamic character of development ecosystems. Artificial intelligence represents a potentially disruptive force within these ecosystems. While current applications primarily automate repetitive or data-intensive tasks, future systems are expected to support decision-making, contribute creatively, and operate as learning entities within development teams. Such transformation requires rethinking roles, workflows, and even the tacit knowledge that underpins engineering expertise. The interplay between human and machine intelligence is poised to redefine not only how products are developed but also how innovation processes are conceived and managed. Effective adaptation to these changes depends on maintaining the internal coherence of the product development ecosystem. Tools and methods must evolve in concert, and their interdependencies must be understood and respected. Changes in one element often necessitate adjustments in others; otherwise, benefits may not materialise, or unintended consequences may arise. Ecosystem resilience depends on recognizing these interconnections and managing change systematically rather than through isolated interventions. Product development is undergoing a paradigm shift that calls for continuous reassessment of practices, tools, and frameworks. Understanding the co-evolution of tools and methods within evolving ecosystems is critical to supporting innovation, improving resilience, and sustaining competitiveness in a rapidly changing environment. A robust response to the ongoing transformation requires interdisciplinary research capable of addressing the socio-technical, organizational, and technological dimensions of change.

Products are often optimized for “most likely” conditions, but unexpected variations can render designs ineffective. Using examples from engineering systems, this paper explores the benefits of leveraging non-linear “payoff functions,” where small changes in conditions lead to disproportionate outcomes. By analyzing the direction and curvature of these functions near observed boundaries, designers could gain an understanding of behavior beyond expected ranges. Non-linear modeling can aid in assessing design margins, especially in long-lived systems. Integrating this approach into design processes can be helpful and effective in considering the “preparedness” of a system in the face of unexpected events of different natures.

Methods comprise a significant part of the knowledge engineers are taught and that they use in professional practice. However, methods have been largely neglected in discussions of the nature of engineering knowledge. In particular, methods prove to be hard to track down in the best-known and most influential typology of engineering knowledge, put forward by Walter G. Vincenti in his book What Engineers Know and How They Know It. This article discusses contemporary views of what engineering methods are and what they contain, how methods (fail to) fit into Vincenti’s analysis, and some characteristics of method knowledge. It argues that methods should be seen as a distinct type of engineering knowledge. While characterizing the knowledge that methods include can be done in different ways for different purposes, the core of method knowledge that does not fit into other categories is explicit ‘how-to’ knowledge of procedures, that draw on other types of knowledge.

Research on Android malware has progressed rapidly, yet the task of distinguishing malicious from benign applications continues to test the limits of automated analysis. Earlier work, dominated by static signature matching, frequently struggles when novel or obfuscated samples appear. Contemporary graph–based pipelines alleviate some of these shortcomings by modelling control and data dependencies, but their reliance on pairwise relations often blurs higher–order interactions that experienced adversaries nurture when crafting evasive variants. These observations motivate a return to first principles: we require representations that faithfully encode behaviours without incurring prohibitive overhead. In this study we revisit the problem through the lens of hypergraph representation learning. Treating an application as a hypergraph allows one to encode joint behaviours—such as the co-invocation of critical API calls inside a single execution context—that cannot be decomposed into simple edges without information loss. Building on this representation, we introduce HGANN-Mal, a Hypergraph Attention Neural Network that adaptively emphasises semantically salient hyperedges while softening the influence of spurious ones. The model derives its signals from static analysis to extract structural and semantic features. Importantly, on the Drebin dataset, HGANN-Mal achieves a Macro-F1 score of 97.8

AbstractWhile the “useful life” of products plays an important role in the balance of sustainability and lifecycle assessment, the concept of durability, as the main measure of useful life, is still ill-defined. This paper critically considers the limitations of the current definitions and approaches to durability, by reflecting on the complex interactions of the viewpoints of engineering design teams, users, society and business economics. A new definition is proposed for durability relating to the useful life goals for a product within its techno-socio-economic context.

Concept selection is one of the most important activities in new product development processes in that it greatly influences the direction of subsequent design activities. As a complex multiple-criteria decision-making problem, it often requires iterations before reaching the final decision where each selection is based on previous selection results. Reusing key decision elements ensures decision consistency between iterations and improves decision efficiency. To support this reuse, this article proposes a fuzzy ontology-based decision tool for concept selection. It models the key decision elements and their relations in an ontological way and scores the concepts using weighted fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). By applying the tool to an example, this article demonstrates how the concepts, criteria, weights, and results generated for one decision can be reused in the next iteration.

Heterogenous flows across system boundaries continue to pose significant problems for efficient resource allocation especially with respect to long term strategic planning and immediate problems about allocation to address particular resource shortages. The approach taken here to modelling such flows is an engineering change prediction one. This enables margin modelling by producing system models in dependency matrices with different linkage types. Change prediction approaches from engineering design can analyse where these bottlenecks in integrated systems would be so that resources can be deployed flexibility to avoid them and address them when they occur. Current state of the art of margin research can be furthered by identifying margins on multiple levels of system composition. It can usefully be complemented by a category theory based approach which allows representation of variable and constant properties of models under changing conditions, and the identification of flows within models. Category theory is useful for formalising such explanatory frameworks as it can both structure systems and permit analysis of their applications in a complementary way.

Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.

AbstractThis research aimed to gain insight into current practices and challenges with respect to the adoption of virtual testing and integration with physical testing in product development processes. A focused workshop investigated industrial perspectives on adopting virtual testing and current challenges. This paper reports the findings from the workshop in which representatives from a range of industries explored how virtual testing is used to support physical testing in their different contexts. This paper discusses the current challenges industries face in adopting virtual testing and changing role of physical testing, with reference to recent literature on physical and virtual testing and supported by an earlier empirical case study. This paper reports areas where more research is needed to support industries in overcoming these challenges.

As most engineering design proceeds by modifying past designs and reusing and adapting existing components and solution principles, a significant part of the knowledge engineers employ in design is encapsulated in the past designs they are familiar with. References to past designs, as well as encounters with them, serve to invoke the knowledge associated with them and constructed from them. This chapter argues that much of this knowledge is tacit consisting in and/or made available by the perceptual recognition of features and situations, using a discussion of design margins to illustrate how engineers use tacit knowledge in reasoning about the properties of new designs.

Android is the most popular smartphone operating system. At the same time, miscreants have already created malicious apps to find new victims and infect them. Unfortunately, existing anti-malware procedures have become obsolete, and thus novel Android malware techniques are in high demand. In this paper, we present Falcon, an Android malware detection and categorization framework. More specifically, we treat the network traffic classification task as a 2D image sequence classification and handle each network packet as a 2D image. Furthermore, we use a bidirectional LSTM network to process the converted 2D images to obtain the network vectors. We then utilize those converted vectors to detect and categorize the malware. Our results reveal that Falcon could be an accurate and viable solution as we get 97.16% accuracy on average for the malware detection and 88.32% accuracy for the malware categorization.

Android malicious applications have become so sophisticated that they can bypass endpoint protection measures. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, thereby raising the need to develop efficient ways to detect Android malware. In this paper, we present Hybroid, a hybrid Android malware detection and categorization solution that utilizes program code structures as static behavioral features and network traffic as dynamic behavioral features for detection (binary classification) and categorization (multi-label classification). For static analysis, we introduce a natural-language-processing-inspired technique based on function call graph embeddings and design a graph-neural-network-based approach to convert the whole graph structure of an Android app to a vector. For dynamic analysis, we extract network flow features from the raw network traffic by capturing each application’s network flow. Finally, Hybroid utilizes the network flow features combined with the graphs’ vectors to detect and categorize the malware. Our solution demonstrates 97.0% accuracy on average for malware detection and 94.0% accuracy for malware categorization. Also, we report remarkable results in different performance metrics such as F1-score, precision, recall, and AUC.

AbstractIn order to ensure successful product development processes, manifold modelling approaches have been developed, which cover a wide range of aspects such as responsibilities, duration of activities and dependencies. Still, an industry standard does not exist. Users of process modelling approaches are driven by different targets depending on the respective role. Currently, practitioners need to evaluate strengths and weaknesses of each approach by themselves and find little guidance for the selection.As a consequence, users might select unsuitable approaches and do not get the expected result. Thus, the intended applications of the model such as analyses or an optimization of the process are hampered. This could heavily affect companies´ success by product or project failures.The paper shows the concept of a recommendation tool that enables a suitable and effective selection of process modelling approaches. Key element is the description of relevant use cases and personas that represent the various needs of both different company types as well as different roles within such as process modellers and users. By identifying the most relevant case, each practitioner will be successfully guided to the most suitable modelling approach.

Publishing in international journals is important but also a tricky task. Before this background, this editorial gives a consolidated overview of key aspects to guide authors along developing and submitting a scientific article in IEEE <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Transactions on Engineering Management</small> . For this, we provide insights into formal requirements, with a major on specific article requirements that must be considered. With two checklists, we summarize key questions which every author should consider before submitting an article to IEEE TEM. Beyond that, the readers might also find these suggestions helpful for any other academic outlet, especially in the field of technological innovation and engineering management.

AbstractApplications of autonomous systems are becoming increasingly common across the field of engineered systems from cars, drones, manufacturing systems and medical devices, addressing prevailing societal changes, and, increasingly, consumer demand. Autonomous systems are expected to self-manage and self-certify against risks affecting the mission, safety and asset integrity. While significant progress has been achieved in relation to the modelling of safety and safety assurance of autonomous systems, no similar approach is available for resilience that integrates coherently across the cyber and physical parts. This paper presents a comprehensive discussion of resilience in the context of robotic autonomous systems, covering both resilience by design and resilience by reaction, and proposes a conceptual model of a system of learning for resilience assurance in a continuous product development framework. The resilience assurance model is proposed as a composable digital artefact, underpinned by a rigorous model-based resilience analysis at the system design stage, and dynamically monitored and continuously updated at run time in the system operation stage, with machine learning based knowledge extraction and validation.

Malicious software, widely known as malware, is one of the biggest threats to our interconnected society. Cybercriminals can utilize malware to carry out their nefarious tasks. To address this issue, analysts have developed systems that can prevent malware from successfully infecting a machine. Unfortunately, these systems come with two significant limitations. First, they frequently target one specific platform/architecture, and thus, they cannot be ubiquitous. Second, code obfuscation techniques used by malware authors can negatively influence their performance. In this paper, we design and implement HawkEye, a control-flow-graph-based cross-platform malware detection system, to tackle the problems mentioned above. In more detail, HawkEye utilizes a graph neural network to convert the control flow graphs of executable to vectors with the trainable instruction embedding and then uses a machine-learning-based classifier to create a malware detection system. We evaluate HawkEye by testing real samples on different platforms and operating systems, including Linux (x86, x64, and ARM-32), Windows (x86 and x64), and Android. The results outperform most of the existing works with an accuracy of 96.82% on Linux, 93.39% on Windows, and 99.6% on Android. To the best of our knowledge, HawkEye is the first approach to consider graph neural networks in the malware detection field, utilizing natural language processing.

Android is the most dominant operating system in the mobile ecosystem. As expected, this trend did not go unnoticed by miscreants, and quickly enough, it became their favorite platform for discovering new victims through malicious apps. These apps have become so sophisticated that they can bypass anti-malware measures implemented to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to come up with an efficient way to detect Android malware. In this paper, we present a novel Natural Language Processing (NLP) inspired Android malware detection and categorization technique based on Function Call Graph Embedding. We design a graph neural network (graph embedding) based approach to convert the whole graph structure of an Android app to a vector. We then utilize the graphs' vectors to detect and categorize the malware families. Our results reveal that graph embedding yields better results as we get 99.6% accuracy on average for the malware detection and 98.7% accuracy for the malware categorization.