A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (1) the function of a device determines what to generalize from its SBF model, (2) the SBF model itself suggests how far to generalize, and (3) the typology of functions indicates what method to use.

This paper illustrates the complexity of pointing as it is employed in a design workshop. Using the method of interaction analysis, we argue that pointing is not merely employed to index, locate, or fix reference to an object. It also constitutes a practice for reestablishing intersubjectivity and solving interactional trouble such as misunderstandings or disagreements by virtue of enlisting something as part of the participants’ shared experience. We use this analysis to discuss implications for how such practices might be supported with computer mediation, arguing for a “bricolage” approach to systems development that emphasizes the provision of resources for users to collaboratively negotiate the accomplishment of intersubjectivity rather than systems that try to support pointing as a specific gestural action.

In this paper, we report on a data analysis process for the automated classification of mechanical components. In particular, here, we describe, how to implement a machine learning system for the automated classification of parts belonging to several sub-categories. We collect models that are typically used in the mechanical industry, and then we represent each object by a collection of features. We illustrate how to set-up a supervised multi-layer artificial neural network with an ad-hoc classification schema. We test our solution on a dataset formed by 2354 elements described by 875 features and spanned among 15 sub-categories. We state the accuracy of classification in terms of average area under ROC curves and the ability to classify 606 unknown 3D objects by similarity coefficients. Our parts’ classification system outperforms a classifier based on the Light Field Descriptor, which, as far as we know, actually represents the gold standard for the identification of most types of 3D mechanical objects.

Early system design analysis and fault removal is an important step in the iterative design process to avoid costly repairs in the later stages of system development. System complexity is increasing with increased use of software to control the physical system. There is a dearth of techniques to evaluate inconsistencies, incompatibility, and fault proneness of the system design in an integrated manner. The early design analysis technique presented in this paper aids a designer to understand the interplay between the multifaceted components and evaluate his/her design in an integrated manner. The technique allows simultaneous propagation of different types of faults from various domains and evaluates their functional impact over a period of time. The structure of the technique is explained using domain-specific conceptual metamodels, whereas the execution is based on the event sequence diagram, which is one of the established reliability and safety analysis techniques. One of the notable features of the proposed technique is the object-oriented nature of the system design representation. The technique is demonstrated with the help of a case study, and the execution results of two scenarios are evaluated to demonstrate the analysis capability of the proposed technique.

The value of sketching in engineering design has been widely documented. This paper reviews trends in recent studies on sketching in engineering design and focuses on the encouragement of sketching. The authors present three experimental studies on sketching that look at (1) sketching assignments and their motivation, (2) the impact of a sketching lesson, and (3) the use of Smartpen technology to record sketching; overall these studies address the research question: Can sketching frequency be influenced in engineering education? Influencing sketching frequency is accomplished through motivation, learning, and use of technology for sketching, respectively. Results indicate that these three elements contribute to the encouragement of sketching in engineering design.

This paper describes a new method for generating the turning-gait of a six-legged robot using a combined genetic algorithm (GA)-Fuzzy approach. The main drawback of the traditional methods of gait generation is their high computational load. Thus, there is still a need for the development of a computationally tractable algorithm that can be implemented online to generate stable gait of a multilegged robot. In the proposed genetic-fuzzy system, the fuzzy logic controllers (FLCs) are used to generate the stable gait of a hexapod and a GA is used to improve the performance of the FLCs. The effectiveness of the proposed algorithm is tested on a number of turning-gait generation problems of a hexapod that involve translation as well as rotation of the vehicle. The hexapod will have to take a sharp circular turn (either clockwise or counter-clockwise) with minimum number of ground legs having the maximum average kinematic margin. Moreover, the stability margin should lie within a certain range to ensure static stability of the vehicle. Each leg of a six-legged robot is controlled by a separate FLC and the performance of the controllers is improved by using a GA. It is to be noted that the actual optimization is done off-line and the hexapod can use these optimized FLCs to navigate in real-world scenarios. As an FLC is computationally less expensive, the proposed algorithm will be faster compared with the traditional methods of gait-generation, which include both graphical as well as analytical methods. The GA-tuned FLCs are found to perform better than the author-defined FLCs.

The design of a new car is guided by a set of directives indicating the target market, specific engineering, and aesthetic constraints, which may also include the preservation of the company brand identity or the restyling of products already on the market. When creating a new product, designers usually evaluate other existing products to find sources of inspiration or to possibly reuse successful solutions. In the perspective of an optimized styling workflow, great benefit could be derived from the possibility of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of resources on the Web and the widespread adoption of computer-assisted design tools have made available huge amounts of data, the utilization of which could be improved by using more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to create digital models conforming to specific styling properties more efficiently. The aim of our research is the definition of a framework that supports (semi)automatic extraction of semantic data from three-dimensional models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then to capture and structure the explicit and implicit elements contributing to the definition of car aesthetics, which can be realistically tackled through computational models and methods. The second step is the definition of a system architecture that is able to transfer such semantic evaluation through the automatic annotation of car models.

This paper describes the use of software agents within an interactive evolutionary conceptual design system. Several different agent classes are introduced (search agents, interface agents, and information agents) and their function within the system is explained. A preference modification agent is developed and an example is given illustrating the use of agents in preference modeling.

We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms. We analyze the characteristics of the two methods on the basis of three floor plan scenarios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally, we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.

The value of comprehensive rationale for documenting a design has long been recognized. However, designers rarely produce detailed rationale in practice because of the substantial time investment required. Efforts to support the acquisition of rationale information have focused on languages and tools for structuring the acquisition process, but still require substantial involvement on the part of the designer. This paper describes an experimental system, the Rationale Construction Framework (RCF), that acquires rationale information for the detailed design process without disrupting a designer's normal activities. The underlying approach involves monitoring designer interactions with a commercial computer-assisted design (CAD) tool to produce a rich process history. This history is subsequently structured and interpreted relative to a background theory of design metaphors that enable explanation of certain aspects of the design process. The framework provides an environment that can acquire rich, meaningful rationale information in a time- and cost-effective manner, with minimal disruption to the designer.

This paper presents a comparison of two previous and separate efforts to develop an ontology in the engineering design domain, together with an ontology proposal from which ontologies for a specific application may be derived. The research contrasts an empirical, user-centered approach to developing the ontology engineering design integrated taxonomies (EDIT) with a theoretical approach in which concepts and relations are elicited from engineering design theories design ontology (DO). The limitations and advantages of each approach are discussed. The research methodology adopted is to map the ontology through examining each of the concepts and relations contained within each of the ontologies DO and EDIT with respect to the other. The comparison process results in an examination of both ontologies, with a few changes resulting from this. The importance of the two different approaches, one that is theoretically sound and another that is applicable, is recognized and argued. Finally, the merged ontology for engineering design is proposed as a template ontology that can be tailored by researchers and practitioners for a specific context.

Functional reasoning (FR) enables people to derive and explain function of artifacts in a goal-oriented manner. FR has been studied and employed in various disciplines, including philosophy, biology, sociology, and engineering design, and enhanced by the techniques borrowed from computer science and artificial intelligence. The outcome of FR research has been applied to engineering design, planning, explanation, and learning. A typical FR system in engineering design usually incorporates representational mechanisms of function concept together with description mechanisms of state, structure, or behavior, and explanations and reasoning mechanisms to derive and explain functions. As for representation, philosophers have long argued whether function of an artifact is a genuine property of it. As for explanation and reasoning, they have produced theories for functional ascription by an external viewer as part of an explanation. To build an FR-based system, the theory based on which the system is built and the underlying assumptions must be explicitly identified. This point is not always clear in the engineering of FR-based systems. Understanding the underlying assumptions, logical formulation, and limitations of FR theories will help developers assessing their systems correctly. The purpose of this paper is to review various FR theories and their underlying assumptions and limitations. This later serves as a benchmark for comparing various FR techniques.

Traditionally, design opportunities and directions are conceived based on expertise, intuition, or time-consuming user studies and marketing research at the fuzzy front end of the design process. Herein, we propose the use of the total technology space map (TSM) as a visual ideation aid for rapidly conceiving high-level design opportunities. The map is comprised of various technology domains positioned according to knowledge proximity, which is measured based on a large quantity of patent data. It provides a systematic picture of the total technology space to enable stimulated ideation beyond the designer's knowledge. Designers can browse the map and navigate various technologies to conceive new design opportunities that relate different technologies across the space. We demonstrate the process of using TSM as a rapid ideation aid and then analyze its applications in two experiments to show its effectiveness and limitations. Furthermore, we have developed a cloud-based system for computer-aided ideation, that is, InnoGPS, to integrate interactive map browsing for conceiving high-level design opportunities with domain-specific patent retrieval for stimulating concrete technical concepts, and to potentially embed machine-learning and artificial intelligence in the map-aided ideation process.

Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

The computer-assisted configuration of progressive die is a computationally mixed problem. This paper describes the progressive die design problem and reviews some existing research related to the configuration of progressive dies. It explains the model-based reasoning approach used to configure the intermediate and final solutions to the design problem. In particular, the various shape representation and spatial reasoning techniques are presented. Finally, a computer software architecture for the die design system is presented.

Design is not done in a vacuum. Engineers often rely on prior designs to make new design decisions instead of solving every new problem from scratch. Prior designs that represent good solutions to the tightly coupled nature of mechanical devices are used as guides. Moreover, prior failures are used to avoid repeating old mistakes. In this paper we present a computer-based approach to exploiting the knowledge embodied in prior designs. Reasoning from design cases requires the ability to use cases, or pieces of cases that realize subfunctions of the device being designed. It is, however, difficult to recognize and retrieve relevant cases or case pieces using a given design specification. Because there is no one-to-one correspondence between the desired behavior of a device and the individual component behaviors, it is often not possible to find relevant design cases by using the given overall behavioral specification as an index into case memory. We approach this problem by elaborating the given behavior specification into a description that gives rise to indices with which relevant components can be retrieved. The elaborations are carried out in a behavior-preserving manner using two transformation operators that (a) rely on physical laws if it is known which ones are relevant, or (b) hypothesize behaviors and then search the case memory for ways in which the required behaviors may be achieved. These two approaches are used opportunistically in CADET, a case-based mechanical design system.

Design protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.

Designers are interacting with an increasing number of digital tools in their design process; however, these are usually in addition to the traditional and ubiquitous paper-based design journals. This paper explores the medium of informal design information and its relationships with sketching behavior over three stages of the design process: preliminary investigation and user needs analyses, concept generation and development, and prototyping and testing. Our test bed consists of tangible, digital, and hybrid design journals collected from four semesters of UC Berkeley's graduate level, multidisciplinary course titled “Managing the New Product Development Process: Design Theory and Methods.” We developed protocols for two categories of analysis: one that codes for the media type of each journal and its content, and another one that characterizes the content within the journal. We found a trend toward hybrid digital–tangible journals for the engineering students over the 4-year period. These hybrid journals exhibited a higher degree of detail over advancing design stages, which has been shown to correlate with improved project performance. We also present several case studies of unusual design journals that illustrate the range of designers' interpretations of design journals as a medium. Based on this descriptive research, features for interactive hybrid tangible–digital design journals are recommended.

Research and industrial practice have produced a host of function models and modeling approaches over the last decades. Each of these is meant to support designers in their design endeavors. Industrial practice is excessively diversified in terms of contextual requirements, aims, and adopted processes; this automatically begs the question which of the existing models should be selected for application in a specific situation. This paper sets out to contribute to this discourse. It strives to benchmark the fairly novel integrated function modeling (IFM) framework against the well-established function structures modeling approach. The paper comparatively investigates the respective capabilities of the approaches, following the benchmarking protocol used earlier in relation to this Special Issue. Function structures are used as reference as they represent one of the most widespread function modeling approaches in research and practice. Both function structures and the IFM framework are exemplarily applied for modeling a glue gun. The gradual generation and refinement of the models is used to showcase their respective benefits and shortcomings. Eventually, the IFM framework is found to excel over function structures in terms of comprehensiveness and support for different types of function analyses. Finally, future research directions are proposed.