The objective of this research is to compare the requirements generated by human participants and large language models (LLMs). Requirements are statements that capture the needs and desires from stakeholders and organize them into design parameters. These statements are expressed in natural language which may lead to incompleteness and ambiguity. Due to the recent advancements in the natural language model such as ChatGPT and Gemini as a tool for requirement generation, this study investigates the quantity, variety and completeness of requirements generated by 66 pre-service engineers and 4 LLMs. This is because in some design projects, stakeholder access may be limited. The results show that pre-service engineers outperformed LLMs in variety, quantity and completeness. Future work could involve developing and comparing true human personas to LLMs.

Effective product development relies on creating a requirements document that defines the product’s technical specifications, yet traditional methods are labor-intensive and depend heavily on expert input. Large language models (LLMs) offer the potential for automation but struggle with limitations in prompt engineering and contextual sensitivity. To overcome these challenges, we developed ReqGPT, a domain-specific LLM fine-tuned on Mistral-7B-Instruct-v0.2 using 107 curated requirements lists. ReqGPT employs a standardized prompt to generate high-quality documents and demonstrated superior performance over GPT-4 and Mistral in multiple criteria based on ISO 29148. Our results underscore ReqGPT’s efficiency, accuracy, cost-effectiveness, and alignment with industry standards, making it an ideal choice for localized use and safeguarding data privacy in technical product development.

This paper presents and experimental study that compares the performance of teams of one, three, and six in terms of generation of requirements from given design prompts. Team size has not been fully explored in the literature in comparative experimental studies for requirements generation. The study was conducted with 116 teams of one, 86 teams of three, and 92 teams of six composed of pre-service engineers in an introductory engineering course. Two prompts were used for the in-class activity. Results indicate that the size of the team did not have significant influence on the number of requirements generated. However, this suggests that there is a difference in efficiency of generating requirements. Analyzing the variety, novelty, and completeness of the requirements generated is reserved for future work. This work helps to lay the foundation for justifying team size.

Requirements engineering is in the design process, translating stakeholder needs into actionable and well-defined specifications. While existing design enablers and tools provide partial solutions, they often fall short in addressing essential aspects such as real-time feedback, lifecycle management, and the use of controlled vocabularies. To bridge these gaps, the Requirements Authoring Design Enabler (RADE), a macro-enabled Excel tool, is presented to support requirement authoring, tracking, and management. RADE integrates features like automated feedback, a dual-mode interface, robust change tracking, and controlled vocabularies. The tool was tested with pre-service engineers with user feedback informing iterative refinements. RADE addresses key challenges in requirements engineering, demonstrating its potential to enhance design outcomes across various domains.

Rapid pace of change and increasing complexity in today’s world demand innovative approaches to product development. Foresight methods enable the anticipation of future scenarios and the derivation of product properties. However, current approaches lack mechanisms to continuously align product development with evolving environment and customer requirements, often resulting in late changes and high costs. Early detection of deviations is needed. This paper presents an approach for continuous monitoring, bridging strategic foresight and the product engineering process (PEP). By analyzing prior work and literature, a process model was developed to identify tipping points where product adaptations are necessary using indications and indicators. Initial evaluation through a case study using coffee machines showed the approach’s usability but improvement potential was also identified.

Secure development is an ever-evolving field that has advanced quickly in recent years with initiatives like Secure Development Lifecycle (SDLC), Development Security Operations (DevSecOps), and Model-Based Security Engineering (MBSE). Despite the persistence of the security and design communities to include security in the design process, significant security breaches continue to occur. Our work reviews existing literature to determine the current state of the research at the intersection of these design and cybersecurity fields and ultimately proposes an integrative and systematic approach for developers to generate design principles that incorporate traceable security. This approach integrates security regulations and design principles and activities, encouraging compliance and security considerations at the earliest stages of the design thinking process.

Topology optimization combined with additive manufacturing enables the creation of complex, high-performance products. However, industrial applications often involve numerous and complex requirements, making it challenging to align the design and manufacturing process to meet all demands. A particular challenge is to determine which requirements should be included in the optimization problem statement. This paper presents a procedure model to integrate requirements and feasibility constraints into the design and manufacturing process. It includes two major steps: organizing requirements and constraints in the process and identifying the problem statement. The procedure is applied to the requirements of an engine bracket of AUDI AG, demonstrating its ability to handle numerous requirements and to specify the problem statement.

The process of gathering needs and generating requirements for design for individuals with special needs can be particularly challenging, and the intended solutions are increasingly evolving into Cyber-Physical-Social Systems (CPSS) further complicating the task. Co-design is the preferred approach but when the primary users are children, the challenges are compounded since they are unable to partner in the design process making the task of eliciting needs further difficult. This paper presents an empirical attempt to collate a master list of requirements for design for children with special needs to aid the design process. The study revealed several lacunae in comprehensibility of Requirements and Criteria, and mapping of the two, prompting further investigation into the hindrances to developing a robust and comprehensive resource for designers by designers.

This paper serves as a template for, and argument to, the engineering design research community to pre-register research studies. Pre-registering allows for a research plan to be validated and results published, no matter the findings. To support pre-registering, we propose a case study to study how individual perspectives and decision-making processes interact as design teams collaborate and reach consensus. We explore how narrative misalignments within a design team—disagreements on the best path forward—are shaped by individual perspectives. Driving requirements, requirements that reflect a designer's prime motivations, are used to shed light on individual priorities. A data collection and analysis plan are introduced to explain how the team will examine how consensus was achieved, which divergent personal interests persist, and how future decision-scenarios might be influenced.

This article presents a domain-specific language for writing highly structured multilevel system specifications. The language effectively bridges the gap between requirements engineering and systems architecting by enabling the direct derivation of a dependency graph from the system specifications. The dependency graph allows for the easy manipulation, visualization and analysis of the system architecture, ensuring the consistency among written system specifications and visual system architecture models. The system architecture models provide direct feedback on the completeness of the system specifications. The language and associated tooling has been made publicly available and has been applied in several industrial case studies. In this article, the fundamental concepts and way of working of the language are explained using an illustrative example.

The use of design methods across multiple design phases of the product development process often leads to inconsistency, the loss of transparency, and the rejection of design methods by practitioners. The authors of this work intend to develop a central modelling approach that supports consistency, based on the integrated function modelling (IFM) framework. Therefore, various design methods from the literature were examined for their techniques and content to identify indicators for supporting consistency. The results led to an enhancement of the IFM framework.

Claims about what justice “requires” and the “requirements” of justice are pervasive in political philosophy. However, there is a highly significant ambiguity in such claims that appears to have gone unnoticed. Such claims may pick out either one of two categorically distinct and noncoextensive kinds of requirement that we call 1) requirements-as-necessary-conditions for justice and 2) requirements-as-demands of justice. This is an especially compelling instance of an ambiguity that John Broome has famously observed in the context of claims about other requirements (notably the requirements of rationality and morality). But it appears to have been overlooked by political philosophers in the case of claims about the requirements of justice. The ambiguity is highly significant inasmuch as failing to notice it is liable to distort our normative thinking about politics and make us vulnerable to certain kinds of normatively consequential errors: both mistakenly drawing inferences about what justice demands of us from claims that certain states or societies are not just; and mistakenly drawing inferences about what states or societies are or would be just from claims that justice does not demand of states or societies that they do certain things. Paying greater attention to the distinction between these two different kinds of requirements and the ways in which they come apart is helpful, not merely in avoiding these distortions and errors, but also in resolving, or at least clarifying, a number of other notoriously murky meta-normative debates, especially various important debates about realism and idealism in political philosophy.

The objective was to evaluate energy partitioning and predict the relationship between metabolizable energy (ME) and digestible energy (DE) in hair sheep fed tropical diets at three feeding levels (maintenance, intermediate and high). To evaluate the energy partition, a database with 114 records (54 non-castrated males and 60 females) from comparative slaughter studies was used. To estimate the ratio ME:DE, 207 observations (74 non-castrated males and 133 females) were used from six studies in a multi-study approach, two indirect calorimetry studies (n = 93) and four comparative slaughter (n = 114), using a mixed model and study as random effect. A simple linear regression equation of the ME against DE was fitted to predict the efficiency of DE to ME conversion. Gas losses were greatest (P < 0.05) for animals fed at maintenance level (7.92% of gross energy intake). The variations of energy losses in the urine were 2.64, 2.06 and 2.08%; faecal losses were 34.37, 37.80 and 36.91% for maintenance, intermediary and high level of feeding, respectively. The regression analysis suggested a strong linear relationship between ME and DE, generating the model ME (MJ/day) = −0.1559 (±0.07525) + 0.8503 (±0.005864) × DE (MJ/day). This study highlights the importance of the relationship ME:DE. Equation/factor 0.85 presented herein is alternative that could be used for the calculation of ME from DE in feedlot diets tropical. In conclusion, we suggest that for hair sheep fed tropical diets the conversion factor 0.85 is more adequate to predict ME from DE.

Intimate partner violence against women (IPVAW) is a public health problem that affects women worldwide. Consequently, victims frequently go to healthcare centers, usually with a cover reason. To address this problem, national and autonomic protocols to respond to IPVAW in health systems have been developed in Spain. In this regard, the role of primary care physicians (PCPs) will be essential for addressing IPVAW, but they could encounter obstacles in doing so. The purpose of this study was to explore how IPVAW is addressed in healthcare centers in Spain. This study synthesized the information available in the protocols to address IPVAW among health care workers in Spain and analyzed it according to World Health Organization (WHO) guidelines. Additionally, PCPs’ perspectives on these protocols and the nature of IPVAW attention from healthcare centers were explored through a focus group. The findings displayed that, although the protocols mostly conform to WHO guidelines, they are insufficient to address IPVAW. Generally, PCPs were unaware of the existence of the protocols and referred to the lack of training in IPVAW and protocol use as one of the main obstacles to intervening, along with a lack of time and feelings as well as cultural, educational, and political factors. The adoption of measures to ensure that PCPs apply these protocols correctly and to approach PCPs’ obstacles for addressing IPVAW in consultations will be crucial for the care of victims.

Digital media are a means to deliver products and services, but also a channel to interact with consumers and a source of information on users’ preferences. Data shared by customers on the web, the User-Generated Content (UGC), can give entrepreneurs a detailed perspective of the market. This work examines an application of Natural Language Processing techniques on UGC to discover insights on users' opinions. We collected more than 13.000 reviews of software from digital stores and review website to gather information on the customers’ perspective and their response to a given marketing strategy in two case studies on digital product's launch. The objective is to give support to two Italian companies in the process of business model development through data-driven evidence. We aim to discover who are the users and which are their needs using a lexicon-based approach to mine unstructured text. The results provide qualitative and quantitative descriptions of the market segments. We propose a method to examine UGC and to explore customers’ behavior on social media. The findings helped managers for the development of their business model, enhancing an informed decision-making process.

Our society is built on engineered systems. Engineers are becoming increasingly concerned with the sustainability of systems, particularly their ability to adapt to a changing world. Recently, there has been increased interest in exploring how design margins provide opportunities for a system change. There have been great developments in determining how design margins can absorb change at a system level, but it is still not clear how design margins might provide change opportunities at a decision variable level. In this paper, we show how system-level margins could be deconstructed to explore what change opportunities they may provide at a decision variable level. We also investigate how the coupling of functional requirements limits how system-level margins can be operationalized. Our analysis suggests that design margins can provide meaningful change opportunities at the decision variable level, but the mechanisms that produce these opportunities are complex. These insights lay the groundwork for future research on mapping and representing design margins in the context of system adaptability.

Currently, engineers need to manually analyse requirement specifications for determining parameters to create geometries in generative engineering. This analysis is time-consuming, error-prone and causes high costs. Generative engineering tools (e.g. Synera) cannot interpret natural language requirements directly. The requirements need to be formalised in a machine-readable format. AI algorithms have the potential to automatically transform natural language requirements into such a formal, machine-readable representation. In this work, a method for formalising requirements for generative engineering is developed and implemented as a prototype in Python. The method is validated in a case example using three products of an automotive engineering service provider. Requirements to be formalised are identified in the specifications of these three products, which are used as a test set to evaluate the performance of the method. The results show that requirements for generative engineering are formalised with high performance (F1 of 86.55 %). By applying the method, efforts and therefore costs for manually analysing requirements regarding parameters for generative engineering are reduced.

Additive manufacturing (AM) processes are now integrated in industry. Therefore, new methods to design AM parts taken into consideration capabilities and limitations are necessary. It is very difficult for teachers to effectively guide students with ideas emerging from generative design tools. AM requires significant preparation and compromises. Topological optimization is also used depending on requirements. A significant impact on the final part quality is related to the part orientation and geometric dimensions. Therefore, this white paper focuses on detailed design steps to prepare future technicians and engineers to design for additive manufacturing. Active teaching pedagogy guideline is proposed. Students have to think in 3D and use analysis tools to create and validate the optimised design. They use immersive tools to review constraints and model diagnostic algorithm to generate data. Present approaches with design guidelines and tools enable to create AM rules based on it. Questionnaire shows that students need explicit knowledge information. Features recognition and geometry diagnostic are mandatory for complex model. Immersive tool helps to evaluate post-processing. They can now relate AM product-process relationship.