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John Grundy

Publications and source records attributed to John Grundy.

At least 19 recordsLinked to original sources

Human-Centred Requirements Engineering for Critical Systems: Insights from Disaster Early Warning Applications

Critical systems, such as those used in healthcare, defence, transportation, and disaster management, require rigorous requirements engineering to ensure safety and reliability. However, this rigour has traditionally focused on technical assurance, with less attention to the human and social contexts in which these systems are used. This paper argues that human-centricity is an essential dimension of dependability and presents a human-centred requirements engineering process for making vulnerable-user needs explicit and traceable from inclusive design guidelines to requirements, prototype features, and validation evidence. Drawing on a structured review of inclusive design literature, we identified 62 guidelines relevant to four vulnerable communities: older adults, low-digital-literacy users, rural users, and colour-blind users. These guidelines were translated into a catalogue of 67 functional and non-functional requirements for inclusive early warning systems. To our knowledge, this is one of the first studies in software engineering to consolidate and empirically validate inclusive design requirements for disaster early warning systems, where accessibility and usability failures can have serious safety consequences. The requirements were operationalised through an adaptive disaster early warning prototype and evaluated through six interviews and eight cognitive walkthroughs. The evaluation provided strong positive evidence across the four vulnerable groups, with particularly encouraging results for elderly and rural users, whose requirements achieved full validation coverage and high positive validation rates. The paper concludes by positioning human-centricity not as an ethical add-on, but as a traceable quality concern in the design of safe and equitable critical systems.

cs.SE

Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective

Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed Socio-Technical Grounded Theory for Data Analysis (STGT4DA) to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain developer flow, improve developer mental models, and foster entrepreneurship) and challenges (e.g., damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as actionable guidances into how mitigate these challenges. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.

cs.SE

Engineering for Crisis Management: A User-Centred Analysis of Disaster Mobile Applications

Disaster mobile apps play an increasingly important role in disseminating hazard information and supporting communities during emergency situations. This study presents a comprehensive analysis of these mobile applications, focusing on their features, user-reported challenges, and opportunities for improvement. We first examined the landscape of disaster mobile apps by analysing 70 apps identified through a combination of methods, including those from the literature, the Google Play Store, and the App Store. The analysis categorised apps based on disaster focus, geographic coverage, popularity, monetisation strategies, and features across the disaster lifecycle. We then extracted, translated and analysed user reviews using topic modelling and sentiment analysis to identify key concerns and recurring issues. The results show that most applications prioritise response-related functionalities, with limited support for preparedness and recovery. User feedback highlights critical challenges related to technical reliability, usability, accessibility, and information clarity. Based on these findings, we propose a set of recommendations for developers and emergency management agencies to improve the reliability, inclusiveness, and overall effectiveness of disaster mobile apps. These include adopting lifecycle-oriented design approaches, strengthening multilingual support, improving technical robustness, and integrating user feedback into development processes. This work contributes to the growing body of research on human-centred disaster risk reduction by providing empirical insights and actionable guidance for the design of more reliable and inclusive disaster communication systems.

cs.SE

SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking

Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW's effectiveness degrades significantly under low-entropy settings such as code generation and mathematical reasoning. A crucial step in the KGW method is random vocabulary partitioning, which enables adjustments to token selection based on specific preferences. Our study revealed that the next-token probability distribution plays an critical role in determining how much, or even whether, we can modify token selection and, consequently, the effectiveness of watermarking. We refer to this characteristic, associated with the probability distribution of each token prediction, as \emph{watermark strength.} In cases of random vocabulary partitioning, the lower bound of watermark strength is dictated by the next-token probability distribution. However, we found that, by redesigning the vocabulary partitioning algorithm, we can potentially raise this lower bound. In this paper, we propose SSG (\textbf{S}ort-then-\textbf{S}plit by \textbf{G}roups), a method that partitions the vocabulary into two logit-balanced subsets. This design lifts the lower bound of watermark strength for each token prediction, thereby improving watermark detectability. Experiments on code generation and mathematical reasoning datasets demonstrate the effectiveness of SSG.

cs.CR

Requirements Perception Gap across Stakeholders: A Comparative Survey of Aged Care Digital Health Software

We sought to explore and compare the perspectives of three key stakeholder groups: older adults, caregivers (formal health providers and informal caregivers), and digital health software developers on key functional and non-functional requirements. We conducted a survey, designed based on the findings from an existing systematic review, to gather and analyse data related to the three stakeholder groups' (dis)satisfaction with current aged care digital health software and their views on key future aged care software requirements. A mixed-methods survey approach integrated quantitative questionnaire data and qualitative open-ended responses from a total sample of 249, comprised of older adults (103), formal and informal caregivers (41), and software developers (105). Data analysis utilised a mixed methods approach, employing inferential statistics to compare group satisfaction levels and thematic analysis for qualitative open-ended responses. Our analysis reveals a significant "Requirements Gap". Software developers tend to prioritise advanced features and functional requirements, significantly overestimating user satisfaction with core NFRs such as ease of use and responsiveness. Conversely, developers were more critical of existing functional features compared to older adults and caregivers, who prioritised simplicity and reliability over feature density. By combining quantitative and qualitative analysis, we identified where stakeholder priorities align and where they diverge across functional and non-functional requirements in both the current designs they used and the future designs they desire. Our findings present a stakeholder gap analysis that can guide future co-design processes, near-term product decisions, and privacy-by-design recommendations in aged care digital health.

cs.SE

Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering

How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software developers on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can help practitioners, educators, and policymakers promote responsible and effective use of AI tools.

cs.SE

Towards Explainable Stakeholder-Aware Requirements Prioritisation in Aged-Care Digital Health

Requirements engineering for aged-care digital health must account for human aspects, because requirement priorities are shaped not only by technical functionality but also by stakeholders' health conditions, socioeconomics, and lived experience. Knowing which human aspects matter most, and for whom, is critical for inclusive and evidence-based requirements prioritisation. Yet in practice, while some studies have examined human aspects in RE, they have largely relied on expert judgement or model-driven analysis rather than large-scale user studies with meaningful human-in-the-loop validation to determine which aspects matter most and why. To address this gap, we conducted a mixed-methods study with 103 older adults, 105 developers, and 41 caregivers. We first applied an explainable machine learning to identify the human aspects most strongly associated with requirement priorities across 8 aged-care digital health themes, and then conducted 12 semi-structured interviews to validate and interpret the quantitative patterns. The results identify the key human aspects shaping requirement priorities, reveal their directional effects, and expose substantial misalignment across stakeholder groups. Together, these findings show that human-centric requirements analysis should engage stakeholder groups explicitly rather than collapsing their perspectives into a single aggregate view. This paper contributes an identification of the key human aspects driving requirement priorities in aged-care digital health and an explainable, human-centric RE framework that combines ML-derived importance rankings with qualitative validation to surface the stakeholder misalignments that inclusive requirements engineering must address.

cs.SE

Auto-Generating Personas from User Reviews in VR App Stores

Personas are a valuable tool for discussing accessibility requirements in software design and development practices. However, the use of personas for accessibility-focused requirements elicitation in VR projects remains limited and is accompanied by several challenges. To fill this gap, we developed an auto-generated persona system in a VR course, where the personas were used to facilitate discussions on accessibility requirements and to guide VR design and development. Our findings indicate that the auto-generated persona system enabled students to develop empathy more efficiently. This study demonstrates the use of automatically generated personas in VR course settings as a means of eliciting latent accessibility requirements.

cs.HC

Role of Graphics in Disaster Communication: Practitioner Perspectives on Use, Challenges, and Inclusivity

Information graphics, such as hazard maps, evacuation diagrams, and pictorial action guides, are widely used in disaster risk communication. These visuals are important because they convey hazard information quickly, reduce reliance on lengthy text, and support decision-making in time-critical situations. However, despite their importance, disaster information graphics do not work equally well for all audiences. In practice, many graphics remain difficult to interpret, and their accessibility for vulnerable populations is still uneven and underexplored. Despite their central role, there has been little empirical work examining how graphics shape disaster communication, what challenges practitioners face in using them, and, most importantly, how inclusive current disaster graphics are in real-world settings. To address this gap, we examine how information graphics are currently produced and used in disaster communication, what issues emerge in practice, and how inclusivity is addressed. We conducted semi-structured interviews with disaster communication practitioners and researchers to examine the role of graphics across preparedness, warning, and response contexts, as well as the barriers experienced by vulnerable communities. Our findings show that graphics are widely expected and heavily relied upon, yet significant accessibility gaps persist for groups such as people with vision impairments, older adults, and culturally and linguistically diverse communities. Participants also highlighted that inclusive adaptations are difficult to achieve during unfolding emergencies due to operational constraints, limited guidance, and resource barriers. Based on these findings, we outline recommendations for disaster management agencies and graphic designers and identify research directions for technological and adaptive support to make disaster graphics more inclusive at scale.

cs.GR

SOGPTSpotter: Detecting ChatGPT-Generated Answers on Stack Overflow

Stack Overflow is a popular Q&A platform where users ask technical questions and receive answers from a community of experts. Recently, there has been a significant increase in the number of answers generated by ChatGPT, which can lead to incorrect and unreliable information being posted on the site. While Stack Overflow has banned such AI-generated content, detecting whether a post is ChatGPT-generated remains a challenging task. We introduce a novel approach, SOGPTSpotter, that employs Siamese Neural Networks, leveraging the BigBird model and the Triplet loss, to detect ChatGPT-generated answers on Stack Overflow. We use triplets of human answers, reference answers, and ChatGPT answers. Our empirical evaluation reveals that our approach outperforms well-established baselines like GPTZero, DetectGPT, GLTR, BERT, RoBERTa, and GPT-2 in identifying ChatGPT-synthesized Stack Overflow responses. We also conducted an ablation study to show the effectiveness of our model. Additional experiments were conducted to assess various factors, including the impact of text length, the model's robustness against adversarial attacks, and its generalization capabilities across different domains and large language models. We also conducted a real-world case study on Stack Overflow. Using our tool's recommendations, Stack Overflow moderators were able to identify and take down ChatGPT-suspected generated answers, demonstrating the practical applicability and effectiveness of our approach.

cs.SE

VRARE: Using Virtual Reality to Understand Accessibility Requirements of Color Blindness and Weakness

In this paper, we developed a virtual reality (VR) system that can simulate color blindness and weakness. We built an immersive 3D web view interface where participants can discuss accessibility requirements for a fitness website projects within a virtual fitness environment. We conducted a pilot experiment involving 24 participants from six software teams, who used both VR and non-VR methods to understand color blindness and weakness requirements in a website project. Our findings indicate that using VR can provide several benefits for requirements activities, such as an improved user experience and reduced workload.

cs.HC

Discussing Your Needs in VR: A Novel Approach through Persona-based Stakeholder Role-Playing

In this study, we propose a novel approach that supports requirements discussions in virtual environments by automatically generating personas from real-time speech-to-text data. In our pilot experiment, 18 participants (14 from universities and 4 from IT companies) used the generated personas to discuss accessibility requirements within the virtual environment. Participants reported a relatively high level of satisfaction with the social presence and usability of the VR system. We also found that requirements discussions based on personas have a lower workload. Finally, we outline the main directions for future work.

cs.HC

A Proxy Stakeholder Approach to Requirements Engineering for Inclusive Navigation

Wayfinding, or the ability to navigate one's surroundings, is crucial for independent living and requires a complex combination of cognitive abilities, environmental awareness, and technology to manage this successfully. Individuals with cognitive impairment (IwCI) often face significant challenges in learning and navigating their environment. Despite its importance, mainstream navigation technologies are rarely designed with their diverse needs in mind. This study reframes the search for places as a socially distributed task and emphasizes the role of proxy stakeholders, who act on behalf or in coordination with IwCI during navigation. Using a qualitatively led mixed-methods approach, which includes an international survey and a three-stage interview study, we examine the real-world strategies that proxy stakeholders employ to support daily navigation. The findings are synthesized into a set of empirically grounded design recommendations that emphasize customisability, collaborative use, and support for routine-based navigation. Our findings highlight key challenges and adaptive practices, which are synthesized into design recommendations that prioritize customisability, routine-based navigation, and multi-user coordination. By introducing the proxy stakeholder concept into the software engineering literature, we propose a more inclusive approach to requirements elicitation and offer practical guidance for designing navigation technologies that better reflect the complex realities of cognitive support.

cs.SE

Elderly HealthMag: Systematic Building and Calibrating a Tool for Identifying and Evaluating Senior User Digital Health Software

Digital health (DH) software is increasingly deployed to populations where many end users live with one or more health conditions. Yet, DH software development teams frequently operate using implicit, incorrect assumptions about these users, resulting in products that under-serve the specific requirements imposed by their age and health conditions. Consequently, while software may meet clinical objectives on paper, it often fails to be inclusive during actual user interaction. To address this, we propose \textbf{\textit{HealthMag}}, a tool inspired by GenderMag designed to help better elicit, model and evaluate requirements for digital health software. We developed HealthMag through systematic mapping and calibration following the InclusiveMag framework. Furthermore, we integrated this with a calibrated version of an existing AgeMag method to create a dual-lens approach: \textbf{\textit{Elderly HealthMag}}, designed to aid requirements, design and evaluation of mHealth software for senior end users. We demonstrate application and utility of Age HealthMag via cognitive walkthroughs in identifying inclusivity biases in current senior user-oriented digital health applications.

cs.SE

How Does Cognitive Capability and Personality Influence Problem Solving in Coding Interview Puzzles?

Software engineering involves cognitively demanding activities impacted by individual differences. We investigate how cognitive capability and personality traits are associated with software problem solving accuracy. We assessed cognitive capability using Baddeley's three-minute grammatical reasoning test. Personality was measured using the IPIP-NEO-50 test. Eighty participants (40 software practitioners and 40 software engineering students) completed these two tests with nine interview-style problem solving tasks, comprising six coding-related and three logical-reasoning questions. Our practitioners achieved slightly higher grammatical reasoning accuracy than students, although this difference was not statistically significant. Students achieved higher accuracy on the coding and logical-reasoning tasks. For all, grammatical reasoning accuracy was positively correlated with problem solving accuracy, indicating that individuals with higher reasoning accuracy tended to perform better on applied problem solving tasks. Conscientiousness was the strongest personality-related association, positively correlated with both grammatical reasoning and problem solving accuracy. Openness to experience was also positively correlated with grammatical reasoning and problem solving accuracy. Neuroticism showed a small negative correlation with problem solving accuracy and weak negative correlation with grammatical reasoning accuracy. Practical implications for education and industry include integrating structured reasoning tasks in curricula, and considering the interplay of personality and cognition in recruitment and role allocation.

cs.SE

Toward Agentic Software Project Management: A Vision and Roadmap

With the advent of agentic AI, Software Engineering is transforming to a new era dubbed Software Engineering 3.0. Software project management (SPM) must also evolve with such transformations to boost successful project completion, while keeping humans at the heart of it. Building on our preliminary ideas of "agentic SPM", and supporting literature, we present our vision of an "Agentic Project Manager (PM)" as a multi-agent system for SPM 3.0. They will work like a "junior project manager", or an "intern project manager" collaboratively with software teams. We introduce four working modes, with varying autonomy levels to choose from, based on the SPM task. This addresses concerns with ethics, accountability, and trust related to agentic PMs. We also share insights on human PM role evolution and new skill requirements as a "strategic leader" and a "coach" for humans and agents. While creating the foundation for agentic SPM research, we present a research agenda for the wider research community.

cs.SE

Empathy Guidelines for Improving Practitioner Well-being & Software Engineering Practices

Empathy is a powerful yet often overlooked element in software engineering (SE), supporting better teamwork, smoother communication, and effective decision-making.This paper introduces 17 actionable empathy guidelines designed to support practitioners, teams, and organisations. We also explore how these guidelines can be implemented in practice by examining real-world applications, challenges, and strategies to overcome them shared by software practitioners. To support adoption, we present a visual prioritisation framework that categorises the guidelines based on perceived importance, ease of implementation, and willingness to adopt. The findings offer practical and flexible suggestions for integrating empathy into everyday SE work, helping teams move from principles to sustainable action.

cs.SE

User-Centric Requirements Prioritization in mHealth Applications: Insights from a Discrete Choice Experiment

Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a personalized solution to enhance user experience, yet barriers to adoption remain. Understanding user preferences and trade-offs is essential to ensure widespread acceptance of adaptation designs. This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design. A Discrete Choice Experiment (DCE) was conducted with 186 participants who have chronic diseases and use mHealth applications. Participants were asked to select preferred adaptation designs from choices featuring six attributes with varying levels. A mixed logit model was used to analyze preference heterogeneity and determine the factors most likely influencing adoption. Additionally, subgroup analyses were performed to explore differences by age, gender, health conditions, and coping mechanisms. Maintaining usability while ensuring controllability over adaptations, infrequent adaptations, and small-scale changes are key factors that facilitate the adoption of adaptive mHealth app designs. In contrast, frequently used functions and caregiver involvement can diminish the perceived value of such adaptations. This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. Furthermore, our results offer valuable guidance for developing future adaptive mHealth applications and lay the groundwork for continued exploration into requirements prioritization within the field of software engineering.

cs.SE