Requirements Elicitation Research Articles

GoEliTool for Software Requirements Elicitation using Goal-Oriented Approach

Requirements engineering (RE) is an essential initial stage in software engineering. The RE process begins with the elicitation stage. This stage collects all user requirements that must be fulfilled by the system which will be developed. A goal-oriented approach is an effective approach used to automate the RE process. The development of goal-oriented input document standards is one of the important issues that has not been widely studied. Therefore, this study developed a goal-oriented input document standard for the requirements elicitation process. A tool is developed based on the form of the input document that has been generated. The development of standard forms of input documents begins with literature study and data collection, analysis, design of standard forms of documents, tool design, tool development, and testing. At the analysis stage, a logical framework and element structure is formulated in a goal-oriented approach. Furthermore, the standard form of input documents is developed. The standard form of the document becomes a guideline for developing tools to process data requirements from elicitation results. Tool testing is carried out using black-box testing. The test results show that the tool can work according to the planned function. The trial of the use of the tools was carried out using five requirements datasets. The results of testing and using the tool through the requirements dataset show that GoEliTools can be used to record data on the requirements of several users for the development of an information system.

Communication, culture, competency, and stakeholder that contribute to requirement elicitation effectiveness

<span lang="EN-US">In the context of software development, requirement engineering is one of the crucial phases that leads to software project success or failure. According to several disruptive changes in the software engineering landscape as well as the world’s challenge of virus pandemic, the provision of practical and innovative software applications is required. Therefore, issues resolution in requirement elicitation is potentially one of the key success factors resulting in enhanced quality of system requirement. The authors have striven to create new ways of requirement elicitation according to factor effects of communication, culture, competency, and stakeholder, by incorporating tools, processes, methods, and techniques to solve the problems comprehensively, and then proposed an adaptive and applicable conceptual framework. To illustrate these effects, the authors performed a literature review from the past 8 years, and then data analysis from interviews of 27 practitioners, observations and focus groups of software development in real-life projects.</span>

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graphs</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">objects</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">points</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">properties</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">roles</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relationships</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extensions</i> (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MetaGraph</i> , for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.

Features requirement elicitation process for designing a chatbot application

AbstractToday, chatbots are being employed across a wide range of businesses. The researchers will present the outcome of the literature exploration on attributes of chatbots that later become a reference to build a new chatbot for banking use. The goal is to use human natural language as an interaction medium to aid humans in doing any prescribed activity. It is important to think about how people engage with chatbots in order to create a viable and high‐quality chatbot solution. The authors aim to help the chatbot community by determining what qualities a chatbot should have and how to build a chatbot for a bank. For the purpose of compiling useful chatbot characteristics, the researchers searched through a group of linked papers. The survey was done on a small group of chatbot end users in order to get a sense of what functionalities are most important to the end users. On the basis of a summary of the literature research and data from a survey, a set of chatbot functional features is proposed. This research intended to provide a quality feature criterion for creating a chatbot application.

Measuring and improving software requirements elicitation in a small-sized software organization: a lightweight implementation of ISO/IEC/IEEE 15939:2017—systems and software engineering—measurement process

Measuring and improving software requirements elicitation in a small-sized software organization: a lightweight implementation of ISO/IEC/IEEE 15939:2017—systems and software engineering—measurement process

Design and implement a mobile-based system to teach traffic signs to illiterate people

Abstract Nowadays, most information and communication technologies target literate users and neglect the 800 million illiterate individuals around the world. Although there have been several attempts to develop systems that help illiterate people and try to reduce the digital gap between them and those who are literate, it is still challenging to find accessible systems for illiterate people. This research aims to reduce the digital gap between illiterate and literate people in Iraq through developing a mobile application for learning traffic signs. Firstly, a requirement elicitation study was conducted to explore a set of design considerations and build an accessible interface for illiterate people. The study resulted in several design principles, such as using voice instructions, focusing on graphic content, and using the local language. Then, the mobile application was developed based on these design principles. Finally, an evaluation study was conducted to measure the usability of the developed application. The results revealed that the built application is efficient and effective, and the users were extremely satisfied in several aspects, such as easy usage and learnability, and declared that they would use it in the future and recommend it to others.

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach.

The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process. To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders. Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system. The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations. Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients. Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.

Categorisation of Requirements in the Ontology-Based Framework for Employer Information Requirements (OntEIR)

Employer Information Requirements (EIR) are the keystone for developing a successful Building Information Modelling (BIM) project. However, clients’ lack of skill and experience in categorising and defining these requirements often undermines the performance of a construction project and, ultimately, the ability of the finished product to meet their needs. By definition, EIR shortcomings include incomplete and inconsistent requirements and specifications, and whilst some work has been performed to try to address these, this area is still underdeveloped. This paper reports on the development of a transformative approach to the categorisation of requirements in a meaningful way, enabling effective filtering so that stakeholders can access just the information they need for the task at hand. The Ontology-based framework for Employer Information Requirements (OntEIR) seeks to provide a step change in categorisation by identifying ‘static’ and ‘dynamic’ requirements, including related types and sub-types. OntEIR has been rigorously validated by a group of BIM experts, and the results have revealed that this approach to categorisation significantly improved the elicitation and understanding of requirements.

Guidelines adopted by agile teams in privacy requirements elicitation after the Brazilian general data protection law (LGPD) implementation.

The Brazilian General Data Protection Law (LGPD) implementation has impacted activities carried out by the software development teams. Due to it, developers had to become aware of the existing techniques and tools to carry out privacy requirements elicitation. Extending our previous work, we have investigated the actions taken by organizations regarding the LGPD, specifically in software development, considering the perception of agile development teams after two years of the LGPD implementation. In addition, we also investigated the perception of an agile team regarding the practices, techniques, and tools previously cited by practitioners as potential solutions for use in this context, along with techniques already in use in the current context. We have conducted a systematic literature review (SLR) and selected 36 primary studies. Furthermore, we have conducted a survey with 53 IT practitioners and semi-structured interviews with ten practitioners. The LGPD principles are known by most agile teams and are being implemented by the organizations, although the existing tools to support privacy requirements elicitation are still underused by agile teams. Moreover, agile teams consider that software requirements and software construction are the most impacted areas of knowledge by the LGPD, and most of them use user stories in privacy requirements elicitation. Our findings reveal that agile teams and Brazilian organizations are more concerned with user data privacy issues after the LGPD became effective. However, agile teams still face challenges in privacy requirements elicitation.

The Importance of a Usage-Centric Approach

Karl Wiegers, principal consultant with Process Impact and author of 13 books, spoke with host Gavin Henry about common problems including technical debt; staff scaling; iron triangles; changes over the past 50 years; requirements elicitation with use cases; design iteration and abstraction; prototyping; modeling; project management; negotiating around constraints; product scopes; schedules, budgets, and staffing; product quality; teamwork and culture; defining quality; process improvement; and self-learning. This discussion was about some of the topics that I address in Software Development Pearls. This article provide summary excerpts; to hear the full interview, visit http://www.se-radio.net or access our archives via RSS at http://feeds.feedburner.com/se-radio. Robert Blumen

Analyzing Requirements Elicitation Techniques: understanding the use and challenges to strength the requirements phase - Supplementary material

Analyzing Requirements Elicitation Techniques: understanding the use and challenges to strength the requirements phase - Supplementary material

Mobile App Prototype in Older Adults for Postfracture Acute Pain Management: User-Centered Design Approach.

Postfracture acute pain is often inadequately managed in older adults. Mobile health (mHealth) technologies can offer opportunities for self-management of pain; however, insufficient apps exist for acute pain management after a fracture, and none are designed for an older adult population. This study aims to design, develop, and evaluate an mHealth app prototype using a human-centered design approach to support older adults in the self-management of postfracture acute pain. This study used a multidisciplinary and user-centered design approach. Overall, 7 stakeholders (ie, 1 clinician-researcher specialized in internal medicine, 2 user experience designers, 1 computer science researcher, 1 clinical research assistant researcher, and 2 pharmacists) from the project team, together with 355 external stakeholders, were involved throughout our user-centered development process that included surveys, requirement elicitation, participatory design workshops, mobile app design and development, mobile app content development, and usability testing. We completed this study in 3 phases. We analyzed data from prior surveys administered to 305 members of the Canadian Osteoporosis Patient Network and 34 health care professionals to identify requirements for designing a low-fidelity prototype. Next, we facilitated 4 participatory design workshops with 6 participants for feedback on content, presentation, and interaction with our proposed low-fidelity prototype. After analyzing the collected data using thematic analysis, we designed a medium-fidelity prototype. Finally, to evaluate our medium-fidelity prototype, we conducted usability tests with 10 participants. The results informed the design of our high-fidelity prototype. Throughout all the phases of this development study, we incorporated inputs from health professionals to ensure the accuracy and validity of the medical content in our prototypes. We identified 3 categories of functionalities necessary to include in the design of our initial low-fidelity prototype: the need for support resources, diary entries, and access to educational materials. We then conducted a thematic analysis of the data collected in the design workshops, which revealed 4 themes: feedback on the user interface design and usability, requests for additional functionalities, feedback on medical guides and educational materials, and suggestions for additional medical content. On the basis of these results, we designed a medium-fidelity prototype. All the participants in the usability evaluation tests found the medium-fidelity prototype useful and easy to use. On the basis of the feedback and difficulties experienced by participants, we adjusted our design in preparation for the high-fidelity prototype. We designed, developed, and evaluated an mHealth app to support older adults in the self-management of pain after a fracture. The participants found our proposed prototype useful for managing acute pain and easy to interact with and navigate. Assessment of the clinical outcomes and long-term effects of our proposed mHealth app will be evaluated in the future.

Creativity and Design Thinking as Facilitators in Requirements Elicitation

Context: The use of Creativity and Design Thinking (C&amp;DT) techniques favor the generation of new ideas based on the needs of users and stakeholders, and can support software developers during the process of requirements elicitation. Objectives: In this work, we aim to identify C&amp;DT techniques to perform requirements elicitation proposed in the literature and in the industry and investigate the perception of software developers about using these techniques. Methods: We conducted a systematic literature review (SLR) to identify the C&amp;DT techniques in the literature and a regional survey with software development teams from several companies in Brazil to identify which techniques found in the literature are currently being used by organizations. The survey also investigated the level of knowledge that software developers have regarding the C&amp;DT techniques, and whether they agree that the use of these techniques can help to achieve a more effective process of requirements elicitation. Results: In the SLR, we identified 86 C&amp;DT techniques that support requirement elicitation activities. In the survey, most developers outlined that C&amp;DT techniques facilitate requirements elicitation and stated that they have more knowledge and usage experience with DT techniques than creativity techniques. The most used DT techniques mentioned by survey participants were: interview, brainstorming, uses cases, activity analysis, user story, and rapid prototyping, whereas for creativity techniques were: analogies, creativity workshops, focus group, questions list, clarification, none and combining ideas. Conclusions: The results showed that despite the existence of a large number of techniques in the literature, the developers’ lack of knowledge about these techniques makes them not used in the industry. However, the developers’ responses showed that the use of C&amp;DT techniques helps to make requirements elicitation more effective.

DEPENDENCIES BETWEEN REQUIREMENTS ELICITATION TECHNIQUES: A SURVEY STUDY IN UKRAINIAN COMPANIES

The subject of research in the article is requirements elicitation practices in IT projects. The goal of the work is to define how project context influences requirement elicitation technique selection and identify dependencies between requirement elicitation techniques. The following tasks are solved in the article: examine the industrial standards and experience of business analysts and requirements engineers in requirements acquisition activities, create and conduct a survey on practices in requirement elicitation activities in IT projects, define practitioners’ preferences regarding elicitation techniques, and define how project context influences requirement elicitation technique selection, identify dependencies between requirement elicitation techniques. The following methods are used: a survey was performed among business analysts and requirement engineers in Ukraine regarding their use of requirement elicitation techniques and the context of using them. The Chi-Square test of independence and Cramer’s V effect size measure were applied to define statistically significant dependencies between project context and elicitation techniques, as well as dependencies between techniques. The following results were obtained: Top elicitation techniques were identified and compared with other comprehensive studies. Twenty statistically significant associations for pairs "project context – elicitation technique" and "elicitation technique – elicitation technique" were found (based on the p-value and Cramer’s V effect size measure). Conclusion: It is concluded that project context influences particular elicitation technique selection in IT projects. There are also dependencies between requirements elicitation techniques. These dependencies can guide the selection of an initial set of techniques or adjust a set of used elicitation techniques during business analysis planning and monitoring activities.

Monitoring ROS2: from Requirements to Autonomous Robots

Runtime verification (RV) has the potential to enable the safe operation of safety-critical systems that are too complex to formally verify, such as Robot Operating System 2 (ROS2) applications. Writing correct monitors can itself be complex, and errors in the monitoring subsystem threaten the mission as a whole. This paper provides an overview of a formal approach to generating runtime monitors for autonomous robots from requirements written in a structured natural language. Our approach integrates the Formal Requirement Elicitation Tool (FRET) with Copilot, a runtime verification framework, through the Ogma integration tool. FRET is used to specify requirements with unambiguous semantics, which are then automatically translated into temporal logic formulae. Ogma generates monitor specifications from the FRET output, which are compiled into hard-real time C99. To facilitate integration of the monitors in ROS2, we have extended Ogma to generate ROS2 packages defining monitoring nodes, which run the monitors when new data becomes available, and publish the results of any violations. The goal of our approach is to treat the generated ROS2 packages as black boxes and integrate them into larger ROS2 systems with minimal effort.

Participatory user requirements elicitation, validation, and prioritization for tele-otology app in deprived populations

BackgroundThe timely and accurate diagnosis of ENT (Ear, Nose, and throat) diseases in deprived areas is very important due to the increased burden of the disease in high-risk populations. Therefore, the purpose of this study is to identify, validate and prioritize the information needs of stakeholders of a tele-otology system for diagnosing ear diseases and to provide a solution. Material and methodsIn this study, to identify the information need of the stakeholders and prioritize them, the spiral-iterative model was used. Data were obtained through direct observation, interviews with 23 stakeholders (6 ENT specialist doctors, 3 health center managers, 2 patients, and 12 health facilitators), and a survey among all ENT specialists and residents (n = 49), working in Kerman province, Iran. Finally, based on the study findings, the important information needs of the stakeholders were considered as the input for developing the tele-otology software. ResultsIn the observation phase, the workflow of the tele-otology system was developed. In the interview phase, 4 main themes including patient information; Technology and communication; Policy, guidelines, and requirements; Facilitator skills and knowledge were determined. In total, the four categories of the iterative-spiral model including strategic, patient information, commercial and technical with 8, 9, 3, and 9 sub-categories, respectively, were prioritized by specialists which were considered as input for system development. ConclusionBy applying the spiral-iterative model, the prototype included a mobile app and server system was developed. In the final solution, the possibility of capturing patient history and video images was provided for facilitators to help physicians in effective diagnosis. The results of this study, while identifying the workflow of service providers and determining their information needs can help telemedicine system developers to have an optimal design when designing systems for the diagnosis of ear disease.

Testing software's changing features with environment-driven abstraction identification.

ions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance toward requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of <key, value> pairs for better testability, where the “key” helps locate “what to test”, and the “value” helps guide “how to test it” by feeding in concrete data. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by a state-of-the-art technique. While the initial findings indicate our abstractions’ capabilities of revealing bugs and matching the environmental assumptions created manually, we articulate a new way to perform requirements-based testing by focusing on a software system’s changing features. Specifically, we hypothesize that the same feature would behave differently under a pair of opposing environmental conditions and assess our abstractions’ applicability to this new form of feature testing.

Specifying requirements for collection and analysis of online user feedback.

According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.

Deep Learning Model for Selecting Suitable Requirements Elicitation Techniques

Requirement elicitation represents one of the most vital phases in information system (IS) and software development projects. Selecting suitable elicitation techniques is critical for eliciting the correct specification in various projects. Recent studies have revealed that improper novice practices in this phase have increased the failure rate in both IS and software development projects. Previous research has primarily relied on creating procedural systems based on contextual studies of elicitation properties. In contrast, this paper introduces a deep learning model for selecting suitable requirement elicitation. An experiment was conducted wherein a collected dataset of 1684 technique selection attributes were investigate with respect to 14 elicitation techniques. The study adopted seven criteria to evaluate predictive model performance using confusion matrix accuracy, precision, recall, F1 Score, and area under the ROC curve (AUC) and loss curve. The model scored prediction accuracy of 82%, precision score of 0.83, recall score of 0.83, F1 score of 0.82, cross-validation score of 0.82 (± 0.10), One-vs-One ROC AUC score of 0.74, and One-vs-Rest ROC AUC score of 0.75 for each label. Our results indicate the model’s high prediction ability. The model provides a robust decision-making process for delivering correct elicitation techniques and lowering the risk of project failure. The implications of this study can be used to promote the automatization of the elicitation technique selection process, thereby enhancing current required elicitation industry practices.