Requirements Engineering Research Articles

The Evaluation of Quality Engineering for Supply Chain Operations in Khan Dhari Silo / Case Study

The study aims to search for mechanisms to improve the supply chain operations in the silo of Khan Dhari by applying the requirements of quality engineering and focusing on the axes of design, human engineering and value engineering, which are among the most important axes concerned with quality engineering in the silo. The case study approach was relied upon as an approach that helps in the comprehensive and in-depth analysis of the research problem, and the researcher relied on checklists in collecting and analyzing data. The research problem is summarized in the main question (there are failures and stops in the process of receiving the wheat crop, and there is no analysis of the processes of transporting the wheat crop from the processing sources to the storage stage, through circulation. From transportation, weighing and storage tools, and there is no keeping up with the management of supply chains in the silos departments in Baghdad, Including the silo of Khan Dhari with modern technologies), the importance of the research lies in showing how to employ quality engineering and rearrange the technical activities related to receiving grain. The most important results reached are the adoption of advanced strategies related to the processes of receiving the wheat crop and the reliance on an advanced electronic system in the work.

Construction Technology and Service Performance of Waterproof Curtain for Foundation Pit in Large-Particle Pebble Gravel Layer of Yangtze River Floodplain

A foundation pit is constructed in the floodplain of Yangtze River, and a deep and thick layer of large-particle pebble gravel exists below the base slab, thus forming a connected supply channel with the adjacent Yangtze River. The large water volume, high water pressure, and strong permeability of this layer bring great risks to the foundation pit construction. In view of the fact that conventional waterproof curtain construction technologies such as the deep mixing column and high-pressure jet grouting column cannot meet the engineering requirements under these kinds of geological and environmental conditions, a new waterproof curtain construction technology that combines the trenching technology of the diaphragm wall with the TRD (Trench cutting Remixing Deep wall) technology is proposed, i.e., the trenching-and-replacing-style TRD technology, as well as the construction process of this technology, is presented. After the waterproof curtain is built using the proposed technology, the strength, integrity, uniformity, and service performance of the waterproof curtain wall are tested and evaluated by the comprehensive methods of coring, borehole television imaging, resistivity CT, and a group well pumping test. The results show that the proposed technology overcomes the adverse effects of underlying large-particle pebble gravel layer, and the waterproof curtain built by it effectively cuts off the hydraulic connection inside and outside the pit. The technical proposal can provide useful references for similar projects.

Local large language models to simplify requirement engineering documents in the automotive industry

ABSTRACT In automotive engineering, requirements management is crucial for determining the functional and technical qualities of a vehicle and ensuring reproducibility and uniformity throughout the development process. This paper presents a novel and innovative Local GPT Q&A retrieval solution for requirement engineering in the automotive industry. The study demonstrates that leveraging massive language models can significantly simplify the requirements analysis process, providing a more efficient and effective approach to handle complex requirement documents. The evaluation of various language models reveal their exceptional performance in answering evaluation questions, showcasing their potential for automating and enhancing requirement engineering tasks.

A Digital Sexual Health Education Web Application for Resource-Poor Regions in Kenya: Implementation-Oriented Case Study Using the Intercultural Research Model.

Developing a digital educational application focused on sexual health education necessitates a framework that integrates cultural considerations effectively. Drawing from previous research, we identified the problem and essential requirements to incorporate cultural insights into the development of a solution. This study aims to explore the Solution Room of the self-established Intercultural Research Model, with a focus on creating a reusable framework for developing and implementing a widely accessible digital educational tool for sexual health. The study centers on advancing from a low-fidelity prototype (She!Masomo) to a high-fidelity prototype (We!Masomo), while evaluating its system usability through differentiation. This research contributes to the pursuit of Sustainable Development Goals 3, 4, and 5. The research methodology is anchored in the Solution Room of the self-expanded Intercultural Research Model, which integrates cultural considerations. It uses a multimethod, user-centered design thinking approach, focusing on extensive human involvement for the open web-based application. This includes gathering self-assessed textual user feedback, conducting a System Usability Scale (SUS) analysis, and conducting 4 face-to-face semistructured expert interviews, following COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. Based on the identified limitations of the low-fidelity prototype, She!Masomo (SUS score 67), which were highlighted through textual user feedback (63/77) and prototype feature comparisons, iterative development and improvement were implemented. This process led to the creation of an enhanced high-fidelity prototype (We!Masomo). The improved effectiveness of the enhanced prototype was evaluated using the qualitative SUS analysis (82/90), resulting in a favorable score of 77.3, compared with the previous SUS score of 67 for the low-fidelity prototype. Highlighting the importance of accessible digital educational tools, this study conducted 4 expert interviews (4/4) and reported e-survey results following the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guideline. The digital educational platform, We!Masomo, is specifically designed to promote universal and inclusive free access to information. Therefore, the developed high-fidelity prototype was implemented in Kenya. The primary outcome of this research provides a comprehensive exploration of utilizing a case study methodology to advance the development of digital educational web tools, particularly focusing on cultural sensitivity and sensitive educational subjects. It offers critical insights for effectively introducing such tools in regions with limited resources. Nonetheless, it is crucial to emphasize that the findings underscore the importance of integrating culture-specific components during the design phase. This highlights the necessity of conducting a thorough requirement engineering analysis and developing a low-fidelity prototype, followed by an SUS analysis. These measures are particularly critical when disseminating sensitive information, such as sexual health, through digital platforms. RR2-10.1186/s12905-023-02839-6.

Gap distance of UHV substation in areas with an altitude of 5000 m and below

Abstract In view of the increasing energy consumption and power demand, the construction of UHV AC is growing rapidly, and the construction of the project is gradually advancing to higher altitudes. It is urgent to conduct research on typical gap discharge in substations at high altitudes. Combined with the engineering requirements, this paper corrects the test data at an altitude of 0 m according to GB/T 24842 standard through altitude correction and combines the discharge voltage requirements of various typical gaps in substations to obtain the minimum air gap distance in UHV substations at an altitude of 5000 m and below. The calculation results of the two altitude correction methods are compared and analyzed, and the applicability of the two methods is analyzed.

A Real-Time Study on the Cracking Characteristics of Polyvinyl Alcohol Fiber-Reinforced Geopolymer Composites under Splitting Tensile Load Based on High-Speed Digital Image Correlations

The cracking of geopolymer caused by its brittleness characteristics could reduce the stability and durability of the building structure. Studying the cracking behavior of fiber-reinforced geopolymer composites (FRGCs) is important to evaluate the toughness strengthening of geopolymer. This paper presents a real-time study on the cracking characteristics of FRGCs under splitting tensile load based on high-speed digital image correlation (HDIC) technology. The splitting tensile test was conducted on the FRGC with different fiber content. The real-time variation of strain and displacement field during the splitting process was analyzed. The influence of fiber content on the mechanical properties and crack behavior of FRGCs was discussed. Considering the splitting strength and crack width, the optimal fiber content for FRGCs that satisfied the crack resistance requirement was proposed. The results show that the incorporation of fiber can delay the cracking time and reduce strain change during the splitting process. The splitting tensile strength and the deformation increase as fiber content increases, while the crack width decreases as fiber content increases. The FRGC with 2.0% fiber content can maintain a crack width smaller than 0.1 mm, which satisfies the crack resistance requirements of practical engineering for economic consideration.

Effects of Y2Zr2O7 particles and rolling deformation process on the mechanical properties and heat load behavior of W–Y2Zr2O7 plate

This paper addresses the significant requirements in China's fusion engineering and investigates the preparation technology and performance characterization of high-performance tungsten-based materials used as PFMs. The rolled W–Y2Zr2O7 plate were prepared through sintering and subsequent rolling, and steady-state heat load assessment was conducted under fusion device working conditions. Additionally, the change in mechanical behavior of the rolled plates was investigated across different surfaces. The results also revealed that the rolling plate exhibited a DBTT between RT and 200 °C, with more superior mechanical properties along the RD direction. The flat-plate W–Y2Zr2O7/Cu components demonstrated the ability to withstand 1000 heat load cycles at 10 MW/m2 without cracking, indicating favorable thermo-mechanical performance as PFMs. These findings serve as a valuable reference for the fabrication of tungsten-based materials in fusion engineering.

Introduction to thermal control design process for CubeSats in Mexico

Abstract CubeSats are a popular type of spacecraft categorized as nanosatellites. In 1999, Professors first developed these devices at California Polytechnic State University and Stanford University to offer an affordable and accessible experience to students. Data information updated up to May 31st, 2023, shows that 2286 Nanosats have been launched, where 2105 were CubeSats. Moreover, 82 countries have nanosats, whereas Latin America has 44 CubeSat. The fact that Mexico just 5 launched 5 CubeSats is a consequence of the complicated space sector ecosystem in the nation that combines lack of funding and, more importantly, the absence of ¨know-how¨ design space missions from scratch. This manuscript reviews the system engineering methodology used to develop space missions, according to NASA discussing a particular methodology for CubeSat Missions. Next, a CubeSat data trend is shown, followed by a general description of its subsystems. Afterward, the design process activities of the Thermal Control Subsystem (TCS) based on the “NASA Systems Engineering Processes and Requirements” and “Thermal analysis handbook is described. The TCS is often underestimated and confused to implement. For this reason, a design methodology for the TCS accepted to Mexico context is proposed. Finally, the conclusions are presented.

Tools for Requirements Engineering

Tools for Requirements Engineering

A Rapid Review of How Model‐based Systems Engineering is Used in Healthcare Systems

AbstractThis study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability.

Impact Analysis of using Natural Language Processing and Large Language Model on Automated Correction of Systems Engineering Requirements

AbstractThe increasing complexity of Electronic Control Units (ECUs) in the Automotive Industry due to the integration of more sophisticated vehicle features has made the Systems Engineering (SE) application a necessity to define and implement efficient solutions. In this context, requirements emerge as a critical part of the communication between cross‐functional teams. Thus, the more complex systems become, the more requirements are needed to define them. However, lack of information, misalignment and ambiguity on requirements impact the entire development process, resulting in issues later, harder to be fixed. Some studies are being applied to evaluate techniques using Natural Language Processing (NLP) and how it can replace extensive peer reviews, identifying weaknesses in requirements earlier in the process, avoiding wasted time and large financial losses. Normally, NLP is combined with templates such as Easy Approach Requirements to Syntax (EARS), or other rule‐based techniques such as INCOSE's requirements writing best practice rules to define metrics and assess the compliance of the requirements syntax automatically. The focus of this work is to enhance the use of requirements syntax assessment algorithm by combining NLP techniques with Large Language Models (LLMs) to provide automatically corrected requirements.

An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimization (Harris hawks optimization (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback.

Benefits of customizing dust hazard controls to meet your needs

AbstractThe OSHA Combustible Dust National Emphasis Program (NEP), recent studies commissioned by the National Fire Protection Association (NFPA) Research Foundation and U.S. Chemical Safety and Hazard Investigation Board (CSB), as well as emphasis by insurance companies have brought more urgency to combustible dust safety. NFPA standards are being revised for consistency and clarity of standards for combustible dust safety. While that is a significant improvement from conditions of past decades, the situation can be improved even more by implementing risk‐based methods to help develop application‐specific explosion protection. Many facilities that handle combustible particulate solids follow prescriptive requirements of applicable NFPA standards, especially for new installations. Decisions are often driven primarily by meeting codes and standards with minimum capital cost. Legacy installations that do not meet current standards typically claim “grandfather” status as the reason for not implementing the current standard requirement or good engineering practice. The goal of this paper is to present a method of adapting the familiar process safety risk matrix to assess likelihood of a deflagration event rather than the overall the risk of a consequence. This method provides an objective basis to assist staff of a legacy facility to make decisions to prioritize capital improvement projects, to follow current good engineering practice, and to stop relying on “grandfather” status. This paper makes the broad, practical assumption that any deflagration event has serious consequences. The magnitude of those consequences may vary based on facility siting, deflagration area of effect, population of personnel in the affected area, or business criticality of affected equipment and structures. Decisions based solely on the likelihood matrix may inadvertently prioritize a more likely, but less consequential, concern over an event that may put multiple people at risk, or have serious commercial implications. Methods such as Hazard and Operability Study (HAZOP), Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA) and Layer of Protection Analysis (LOPA) are useful to more comprehensively understand risks. Flexible, outside‐the‐box thinking in applying safeguards, Independent Protection Layers (IPLs), administrative controls, and facility siting may provide a better solution than merely following prescriptive code without making the effort to fully understand the hazard.

Replication in Requirements Engineering: The NLP for RE Case

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks’ inherent hairiness , and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card , whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes.

STRENGTH CALCULATION METHODOLOGY FOR THE TECHNOLOGICAL CONTAINERS FOR CHEMICAL HEAT TREATMENT OF WORKPIECES

Purpose. Development of a strength calculation methodology for the special technological containers for chemical heat treatment of workpieces, which considers the consequences of complex influence of the load and the main technological factors. Creation of possibility of full-value mathematical modelling of the special technological containers with complex three-dimensional geometric shape for chemical heat treatment of workpieces using the finite element method. Research methods. Mathematical modeling taking into account the hypothesis of linear failure accumulation from low-cycle fatigue and creep of material. Results. A strength calculation methodology for the special technological containers for chemical heat treatment of workpieces has been developed, which considers the consequences of complex influence of the load and the main technological factors: high-temperature corrosion, interaction with the chemical environment, creep, low-cycle fatigue. This methodology makes it possible to determine safe operation life of the containers. Load combinations have been considered. The main features of the design of the technological containers for chemical heat treatment of workpieces have been considered. Scientific novelty. It has been considered at calculations of the special technological containers the consequences just of the complex influence of the load and the main technological factors which occur during the chemical heat treatment of workpieces: high-temperature corrosion, interaction with the chemical environment, creep, low-cycle fatigue. Practical value. The developed methodology provides the possibility of full-value mathematical modelling of the special technological containers for chemical heat treatment of workpieces using the finite element method. It opens a way for multi-criteria optimization of the design of the containers with complex three-dimensional geometric shape with prospect of reduction of their weight with a certain resource of safe operation. This is one of the main factors of the practical value of this work because the containers for chemical heat treatment of workpieces are made of expensive heat-resistant steels. One factor more is the possibility to determine safe operation life and thus satisfy the requirements of safety engineering.

ReqNet and ReqSim: A Network and Semantic Similarity Dataset of Requirements from the Tree Structure of System Requirement Specifications

Abstract Systems are developed as a solution to the problem space defined by their requirements. The requirements are acquired during the elicitation process. The creative nature of the elicitation process, proprietary nature of requirements, the need of extensive preprocessing and the diverse techniques for analysis restricts the development of a requirement dataset. There exists no standard method to create a requirement dataset. Thus, we devise a semi-formal method to create a multi-purpose requirement dataset that harnesses human knowledge in the system requirement specification documents (SyRSDs) to facilitate the deployment of modern computing algorithms. Our dataset has three forms. (1) ReqList, a list of requirements from 86 distinct systems with their document structure in pure text form. The 12701 requirements are ready to leverage natural language processing techniques and unsupervised machine learning techniques. (2) ReqNet, a large network of requirements consisting of 17375 nodes to deploy graph-theoretic algorithms for requirement engineering. ReqNet portrays small-world network characteristics with an average distance of 9.5619 links. (3) ReqSim, a dataset consisting of 10933 pairs of requirements annotated with their similarity scores. ReqSim enables sentence-level supervised learning tasks to exploit the semantics of requirements. The similarity scores are coherent with human knowledge. Our dataset is grounded by the tree structure of SyRSDs. We devise a method to extract a tree from the SyRSDs. The tree structure resonates with the hierarchical nature of the requirement allocation process. The ReqList, ReqNet and ReqSim dataset is available at https://github.com/ChandanKSahu/ReqList_ReqNet_ReqSim.

Engineering requirements and their role in engineering undergraduates’ design decision making

Engineering requirements and their role in engineering undergraduates’ design decision making

Machine Learning Techniques in Enhanced Oil Recovery Screening Using Semisupervised Label Propagation

Summary Efficiently choosing the optimal enhanced oil recovery (EOR) technique is a critical requirement in reservoir engineering. Machine learning (ML) methods, with a well-established history of application, serve as a swift and dependable tool for EOR screening. In this paper, we aim to evaluate the effectiveness of various ML algorithms for EOR screening, utilizing a comprehensive database of nearly 1,000 EOR projects. This study delves into a comprehensive evaluation of regression and classification-based algorithms to develop a reliable screening system for EOR predictions and address challenges such as limited labeled data and missing values. Our analysis considered various EOR processes, including gas injection, chemical, and thermal EOR techniques. Various ML methods such as random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), shallow artificial neural networks (SANN), naive Bayes classifier (NBC), logistic regression (LR), and decision tree (DT) are applied, enabling both intermethod comparisons and evaluations against advanced methods, multiobjective deep artificial neural networks (MDANN), and multiobjective artificial neural networks (MANN). These advanced techniques provide the unique capability to concurrently address both regression and classification tasks. Considering that conventional methods can only be implemented on a single task, the RF, MANN, MDANN, and KNN algorithms demonstrated top-tier performance in our classification analysis. Regarding the regression task, KNN, RF, and MDANN displayed exceptional performance, signifying their prowess in predictive accuracy. However, MANN exhibited moderate performance in regression analysis. In addition, our study identified areas where certain algorithms, such as support vector regression (SVR), exhibited weaker performance, highlighting the importance of comprehensive model evaluation. This paper contributes novel insights into the application of ML techniques for EOR screening in the petroleum industry. By addressing challenges such as limited labeled data and missing values and by providing a thorough evaluation of various ML algorithms, our study offers valuable information for decision-makers in the oil and gas sector, aiding in the selection of suitable algorithms for EOR projects. In addition, the use of semisupervised label propagation and advanced techniques like KNN imputation adds to the existing body of literature, enhancing the state of knowledge in this domain.

Enhancing the requirements engineering of configurable systems by the ongoing use of variability models

Enhancing the requirements engineering of configurable systems by the ongoing use of variability models

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.