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A more equal and sustainable digital future depends on the inclusion of digitally marginalized communities in the socioeconomic opportunities created by digital technologies. Digital inclusion is a complex process that involves all stages of digital innovation, including development, adoption, use, and maintenance. However, past research has largely approached digital inclusion as an adoption and use challenge. In this paper, we develop a view of digital inclusion as a design challenge. We focus on the activities of requirements elicitation (RE) as a critical element of the design process and draw on a design-based interpretive study involving the design of two mobile apps for agricultural communities in India and China. We analyze how the conditions of digital inequality underlying the digital marginalization of these communities affect their sensemaking as they participate in RE activities. We conceptualize these challenges as limitations on the emergence of technology affordances. Our findings reveal various shifts, or translations, in the emerging affordances, which enabled the RE activities to be more generative and consequently more inclusive. These affordance translations manifested along three main dimensions: specificity, temporality, and collectivity. We discuss the implications of these findings for the inclusion of marginalized communities in the design of new technologies. [ABSTRACT FROM AUTHOR]

Energy exclusion is prevalent throughout Africa, leading to a myriad of energy access projects, technologies, interventions and approaches being explored to address a phenomenon affecting over half the continent's predominantly rural population. This is exhibited in a recent EU-funded energy access project involving partners, researchers and communities working across many African countries employing various approaches. In this paper, we unfold the employment of two divergent approaches, requirements elicitation and community-based co-design, in an attempt to determine and advance green energy inclusion and innovative use in a low-income, off-grid rural Namibian community. Thereafter, we reflect on the use of each approach, highlighting the need for an elevated and provocative approach that enables innovative and unorthodox energy inclusion permitting energy access and productive use in rural African communities. [ABSTRACT FROM AUTHOR]

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity. [ABSTRACT FROM AUTHOR]

The adoption and popularity of mobile devices by end-users is partially driven by the increasing development and availability of mobile applications that can aid solving different problems and provide access to services in a wide range of domains or categories, namely healthcare, education, e-commerce or entertainment. While these applications use and benefit from the combination of a wide panoply of technologies from the Internet of Things, fog and cloud computing, data security and privacy are typically not fully taken into account before the creation of many mobile applications or during the software development phases. This paper presents an in-depth approach to modeling attacks on the specific cloud and mobile ecosystem, given its importance in the process of secure application development. Moreover, aiming at bridging the knowledge gap between developers and security experts, this paper presents an alpha version of the security by design for cloud and mobile ecosystem (secD4CloudMobile) framework. secD4CloudMobile is a set of tools that covers cloud and mobile security requirement elicitation (CMSRE), cloud and mobile security best practices guidelines (CMSBPG), cloud mobile attack modeling elicitation (CMAME), and cloud mobile security test specification and tools (CM2ST). The purpose of the framework is to provide cloud and mobile application developers useful readily applicable information and guidelines, striving to bring security engineering and software engineering closer, in a more accessible and automated manner, aiming at the incorporation of security by construction. Finally, the paper presents some preliminary results and discussion. [ABSTRACT FROM AUTHOR]

Design Thinking techniques have been widely used in software requirements elicitation to understand the necessities of stakeholders and end‐users. However, there is a lack of evidence of their effectiveness when applied to guide the development process of a system targeting vulnerable populations. What are the implications of using Design Thinking techniques to elicit requirements in a community of former inmates—and what would be the benefits of and challenges in this deployment? In this paper, we report our experience of using Design Thinking for requirements elicitation of a mobile application customized for the former inmates of the Brazilian prison system and their families. We applied techniques such as Brainstorming, Stakeholder Mapping, Personas Creation, Rapid Ethnography, and Interviews to obtain relevant data and create several prototypes. These techniques contribute to the development of an uncommon application that aims to help the reintegration process of former inmates into society. Our results validate the initial hypothesis that such techniques, when applied to a sensitive context, assist product development that meets the end‐users' needs by creating a higher quality product. The main limitation of the research was the lack of access to low‐literacy end‐users and/or former inmates without previous experience using mobile devices. [ABSTRACT FROM AUTHOR]

Requirements elicitation is critical phase of software development that involve direct communication of clients and vendors. In global software development (GSD), importance of requirements elicitation becomes more critical due to geographical distance. The aim of this research is to design and implement requirement elicitation model (REM) where GSD organization can evaluate their organizations and can classify their level of implementation where organization lies. Systematic literature review (SLR) was conducted for the identification of critical success factors (CSFs) from a sample of 70 research articles. Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). A second SLR was conducted for identifying all possible practices for the implementation of CSFs. To validate the findings of SLR and to find more practices in the view of industrial practitioners for the identified CSFs, a questionnaire survey was conducted in the GSD organizations in which 35 experts, from different countries, contributed. For REM evaluation, we have conducted two case studies. The outcome analysis after conducting case studies confirms that our assessment framework is easier to understand, apply, and successfully critic the strengths and weaknesses of organizations during requirements elicitation in GSD. [ABSTRACT FROM AUTHOR]

Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas. [ABSTRACT FROM AUTHOR]

Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze, and specify the requirements of enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, and so forth across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for RE for developing VR products requires a detailed study. This article presents a mapping study on RE methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific RE methods in various fields of interest. We also discuss future directions in RE for VR products. [ABSTRACT FROM AUTHOR]

The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying user feedback into requirement and non-requirement categories, the model achieves significant improvements in capturing user sentiments and requirements. The experimental results demonstrate that the model outperforms existing benchmark methods with an accuracy of 95.02%, precision of 94.74%, and recall of 94.53%. These metrics underscore the model's effectiveness in identifying and classifying user requirements accurately. The authors illustrate the proposed methodology through extensive validation and impact analysis, highlighting its effectiveness in dynamically adapting to evolving user feedback. This approach enhances the agility and user-centered nature of software development, ensuring more responsive and accurate requirement elicitation. The novelty of this research lies in the integration of automated feedback collection, advanced preprocessing techniques, and the use of GNN for feature extraction. This combination allows the model to consider the complex and interconnected data structures typical of social media content, leading to superior performance in sentiment analysis and requirement elicitation. The new method's effectiveness is further confirmed by calculations of performance metrics such as precision, recall, and F1-score. The proposed methodology's practical implications are vast, offering a potent solution for agile and user-centered software development practices. By dynamically integrating real-time user feedback, the model supports continuous software improvement and adaptation, making it highly relevant for rapidly changing user preferences and requirements. This research contributes significantly to the fields of sentiment analysis, machine learning, and software engineering, providing a robust framework for future studies and practical applications in requirement elicitation from social media. [ABSTRACT FROM AUTHOR]

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.

Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.

Effective requirements elicitation and validation are crucial for developing healthcare applications that genuinely meet the needs of end users. This study investigates the role of healthcare professionals in these processes and explores the application of openEHR archetypes to enhance stakeholder engagement. A comprehensive literature review identified significant gaps in the participation of domain experts throughout the software development lifecycle. A thematic analysis of survey responses from healthcare professionals revealed vital challenges they face with existing software applications. Statistical analyses confirmed the limited involvement of these professionals, while interviews with software engineers provided insights into the practical implications of integrating archetypes into development processes. Based on these findings, a set of good practices is proposed to facilitate effective requirements elicitation and validation through the active involvement of healthcare professionals. Utilizing openEHR archetypes, these practices aim to bridge the gap between technical specifications and clinical needs, ultimately leading to the development of more user-centered healthcare software solutions. This study highlights the importance of fostering collaboration between developers and domain experts to enhance the quality and usability of healthcare applications, paving the way for improved patient outcomes and operational efficiency.

In agile software development processes, user stories (US) had been used to specify application functionalities from the users’ perspective. For intelligent applications leveraging artificial intelligence (AI), the Smart Product Backlog (SPB) has included both AI-implementable and non-AI functionalities. This paper had proposed a model employing supervised machine learning techniques to classify US descriptions based on their technical feasibility for AI implementation. This model had aimed to assist in constructing smart product backlogs (SPB). Classifying US in agile development, particularly with AI, had been a labor-intensive process demanding significant time and expert involvement. Additionally, the lack of a dedicated dataset for this task had limited the applicability of automated methods. This study addressed this challenge by having experts classify the Mendeley US dataset using binary classification (AI and non-AI). The proposal had involved developing an automatic classification model to process US descriptions and distinguish those suitable for AI implementation. This model had leveraged advanced text processing techniques to refine the textual features of the US. Additionally, it had employed three binary classification techniques: logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). The model’s performance had been evaluated using metrics such as accuracy, loss (Log-Likelihood Loss), precision, recall, F1 score, area under the ROC curve, and specificity to identify the best-performing technique in binary classification. Logistic regression and SVM models had demonstrated high accuracy, with scores of 0.748 and 0.740, respectively. These results had highlighted the potential of an automated tool for recommending US feasible for AI development, thereby supporting decision-making in agile software projects.

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories. Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. [ABSTRACT FROM AUTHOR]

People see the world and convey their perception of it with narratives. In an information system context, stories are told and collected when the systems are developed. Requirements elicitation is largely dependent on communication between systems designers and users. Thus, stories have a significant impact on conceptualizing future users' needs. This paper presents a literature review on how stories and narratives have been considered in central IS literature. Narrative-theoretical parameters are used as a lens to analyze the literature. This shows that explicit discussion is non-existent, and the characteristics are considered partially. The result is a biased and narrow understanding of the informants' needs and wishes. This may be significant in the requirements because narratives are not as simple a form of communication as is usually assumed. It is proposed that better understanding narratives would equip systems analysts with an in-depth understanding about the nuances inherent in communication when communicating with users. [ABSTRACT FROM AUTHOR]

Errors often occur during the requirements elicitation stage, causing failure of the software development process as a whole, so that the built system cannot be used optimally; these data are obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply the elicitation requirements using the KAOS method in the case study of the SMM reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated that include automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved.

Despite noble intentions, new technologies may have adverse effects on the resilience of the sociotechnical systems into which they are integrated. Our objective was to develop a lightweight method to elicit requirements that, if implemented, would support sociotechnical system resilience. We developed and piloted the Resilience-Aware Development Exercise Protocol (RAD-XP), a method to generate tabletop exercises (TTXs) to elicit resilience requirements. In the pilot study, this approach generated 15 requirements from a one-hour TTX, where the majority of requirements were found to support resilience. Participants indicated via survey that RAD-XP was effective and efficient, and that they would want to use RAD-XP regularly throughout the agile development process. We discuss future research and development to refine this approach to eliciting resilience requirements. [ABSTRACT FROM AUTHOR]

Requirements engineering (RE) is a significant aspect of system development stages in generating reliable software (SW). Despite RE's decisive impact on project success, SW systems still fail since there is a perplexity in sorting out requirements correctly. Researchers have tried several paradigms to deal with the specified challenges, such as agent-oriented RE (AORE), model-based RE, and service-oriented RE (SORE). By investigating the limitations of the independent use of these paradigms, this research sets an objective that proposes a framework which integrates the two paradigms (agent and service) on top of social media to enhance the SW RE processes. Thus, the research addresses challenges in gathering adequate requirements, detecting alignment between business requirements and SW products, prioritizing requirements, and recommending innovative ideas. The research has mainly adopted an empirical research methodology for SW engineering. Accordingly, two distinct expert groups have been formed based on their previous experience in AORE and SORE, respectively. The experts have been selected from enterprises and academic institutions, and they participated in our case study. After performing the necessary assessment based on specified criteria, those experts in the first group have reported that CASCRE (Collaboration of Agents and Services for Crowd-based Requirements Engineering) with a score of 93.7% is found to be better than that of AORE with a score of 88.7%. Moreover, experts in the second group have declared that CASCRE, with a score of 92.3%, is better than SORE, with a score of 83.7%. In both cases, improvements have been observed, which reveals that the synergy of the CASCRE features has a better impact on the RE process than utilizing individual approaches. Moreover, in order to demonstrate the applicability of CASCRE, feedback has been gathered from a focused crowd of local pharmaceuticals using a mini-prototype. Accordingly, 250 requirements related comments have been gathered from the discussion forum, and 1400 keywords were generated. Then, after performing a sentiment analysis using NLP algorithms, the result was demonstrated to experts. Therefore, 93% of gurus strongly agreed on the applicability of CASCRE in real projects. [ABSTRACT FROM AUTHOR]

In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm's superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs. [ABSTRACT FROM AUTHOR]

Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a systematic literature review to discover methods to understand business processes and elicit requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges and opportunities. The challenges of developing a business process automation software include dealing with business processes, meeting the needs of the organization, choosing the right approach, and adapting to changes in the process during the development. These challenges open opportunities for proposing specific approaches to elicit requirements in this context.

The use of data analytics and Machine Learning (ML) branches of AI for predictive and analytic knowledge retrieval has surged significantly in various industries (e.g., health, finance, business, and manufacturing). However, the acceptance of AI has been hindered by opaque models that lack transparency. Explainability in AI (XAI) has gained significant prominence owing to its focus on introducing avenues of accountability in AI. XAI acknowledges the importance of human factors and strives to incorporate them into the design process, recognising that the cognitive effort involved in understanding explanations is a key aspect. Mental Models play a crucial role in the XAI evaluative premise, but their current utility is limited. By intentionally designing explanations that align with users’ mental models, their experiences can be significantly enhanced, leading to improved understanding, satisfaction, trust, and performance. This study proposes using Mental Models to elicit explainability requirements and to develop an Ontology-Driven Conceptual Model to facilitate the learning process for a better understanding of explanations.

Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a systematic literature review to discover methods to understand business processes and elicit requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges and opportunities. The challenges of developing a business process automation software include dealing with business processes, meeting the needs of the organization, choosing the right approach, and adapting to changes in the process during the development. These challenges open opportunities for proposing specific approaches to elicit requirements in this context. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to select and prioritize the requirement items in the requirement document. We use an automated requirement method that collects requirements from different stakeholders groups and categories. The new approach is based on assigning an importance factor to each stakeholder group and an acceptance weight for each requirement item. The importance factor and the acceptance weight for each requirement are used to decide whether to include or exclude this requirement item based on a proposed formula. This method is validated by using a case study based on a set of examples, and shows a significant improvement in software requirements process. [ABSTRACT FROM AUTHOR]

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