Context: 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 promising approach to personalizing interactions and improving user experience. However, their adoption remains limited, partly due to a lack of understanding of how users perceive and evaluate different adaptation strategies. Addressing this gap is crucial for advancing user-centered design and requirements engineering in software systems for health contexts. Objective: This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design. Method: A Discrete Choice Experiment (DCE) was conducted with 186 participants living with chronic conditions who regularly use mHealth applications. Each participant completed a series of choice tasks, selecting their preferred adaptation designs from scenarios composed of six attributes with varying levels. A mixed logit model was applied to examine preference heterogeneity. Subgroup analyses were also conducted to explore variations in preferences across age, gender, health condition, and coping mechanism. Results: Participants preferred adaptation designs that preserved usability, offered controllability, introduced changes infrequently, and applied small-scale modifications. Conversely, adaptations affecting frequently used functions and those involving caregiver input were generally viewed less favorably. These findings highlight key trade-offs that influence user acceptance of adaptive mHealth interfaces. Conclusion: 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. These insights provide actionable guidance for designing more user-centered adaptive interfaces and contribute to advancing requirements prioritization practices in software engineering—particularly in the context of health technologies.

Mobile Health (mHealth) applications have demonstrated considerable potential in supporting chronic disease self-management; however, they remain underutilized due to low engagement, limited accessibility, and poor long-term adherence. These issues are particularly prominent among users with chronic disease, whose needs and capabilities vary widely. To address this, Adaptive User Interfaces (AUIs) offer a dynamic solution by tailoring interface features to users’ preferences, health status, and contexts. This article presents a two-stage study to develop and validate actionable AUI design guidelines for mHealth applications. In stage one , an AUI prototype was evaluated through focus groups, interviews, and a standalone survey, revealing key user challenges and preferences. These insights informed the creation of an initial set of guidelines. In stage two , the guidelines were refined based on feedback from 20 end users and evaluated by 43 software practitioners through two surveys. This process resulted in nine finalized guidelines. To assess real-world relevance, a case study of four mHealth applications was conducted, with findings supported by user reviews highlighting the utility of the guidelines in identifying critical adaptation issues. This study offers actionable, evidence-based guidelines that help software practitioners design AUI in mHealth to better support individuals managing chronic diseases.

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed “GustosonicSense”, a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food.

Personas, a user characterisation, have been widely used in requirements engineering (RE) to enhance the understanding of end-users and their needs. However, the persona generation process is time-consuming and demands familiarity with a user-centered approach. The central issue lies in existing tools for automatically generating personas, which are restricted to generating persona templates and provide limited user control to tailor personas according to their specific needs. This paper introduces CRAFTER, a persona generation tool that uses Large Language Models (GPT-3.5 model). This tool not only automates persona creation but also offers recommendations to users for generating personas tailored to their requirements. The study involved an online questionnaire with 19 respondents who utilised the tool, providing feedback that indicated the tool’s sufficiency for persona generation while identifying areas for improvement. Beyond its primary function, CRAFTER stands out by providing guidance to requirements engineers throughout the persona creation process. The tool grants users the flexibility to customise personas based on their specific requirements, acknowledging the crucial human subjectivity in persona development. Additionally, CRAFTER promotes persona reusability, allowing users to save and reuse generated personas for future projects.

Personas, as a tool for characterising end-users, are widely utilised in requirements engineering (RE), primarily to enhance the understanding of end-users and their needs. However, the efficacy of persona usage in RE practice remains inadequately explored. To bridge this gap, we conducted an interview-based study with 22 international RE experts. These experts shared their experiences in utilising personas for RE-related tasks. Through thematic analysis of the collected insights, we propose recommendations for the effective creation and integration of personas in RE tasks. Our suggestions for efficient persona usage in RE emphasise the necessity of direct human interaction throughout persona development, while also con-sidering project constraints. We recommend creating personas that reflect the needs and preferences of persona consumers regarding the information included and the presentation style. Additionally, fostering organisational awareness of the benefits of personas in understanding targeted end-users is crucial. Lastly, we highlight the importance of the interpersonal skills required by requirements engineers to support the successful incorporation of personas in RE.

Empirical software engineering researchers are committed to investigating better ways of improving software engineering practice. To do this, gathering data about software professionals’ experiences and perspectives on the researched topic is crucial. However, in the last decade, our experience of recruiting software professionals as potential participants in our studies has met with fluctuating success. Therefore, we had to explore multiple possibilities when recruiting participants. This cost us more effort and time than anticipated. In this paper, we briefly discuss the challenges we faced when recruiting participants for empirical research in software engineering, the solutions we implemented to address these challenges, and a concise list of strategies for garnering the interest of potential participants.

This paper shares insights from our first-hand experience with key recruitment challenges encountered in software engineering research, focusing on two distinct participant groups: end-users and software practitioners. By conducting a reflective analysis, we emphasise the particular challenges we faced when engaging these groups during empirical study recruitment phases. Significant challenges we faced in recruiting end-users include ensuring authenticity, maintaining engagement, achieving demographic diversity, and addressing privacy concerns. Conversely, we faced different challenges when recruiting software practitioners, including sourcing the right expertise, utilising online recruiting platforms, navigating time constraints, aligning incentives, obtaining a representative sample, and coordinating with remote and distributed teams. By detailing the strategies we employed to address these challenges, this paper contributes practical knowledge to enhance the efficacy and inclusiveness of research practices, ultimately fostering more robust software engineering research outcomes.

As software systems become more complex, some users find it challenging to use these tools efficiently, leading to frustration and decreased productivity. We tackle the shortcomings of conventional user support mechanisms in software and aim to create and assess a user support system that integrates Multimodal Large Language Models (MLLMs) for producing support messages. Our system initially segments the user interface to serve as a reference for selection and requests users to specify their preferences for support messages. Following this, the system creates personalised user support messages for each individual. We propose that user support systems enhanced with MLLMs can provide more efficient and bespoke assistance compared to conventional methods.

Abstract eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications.

Personas have been widely used during requirements engineering-related tasks. However, the presentation, composition, level of details and other characteristics varies greatly by domain of use. To better understand these, we formed a curated set of nearly 100 personas from 41 academic papers and analysed their similarities and differences. We then used our analysis to formulate a preliminary taxonomy of personas used for Requirements Engineering-related tasks. We describe our key findings from our analysis with examples, our preliminary taxonomy, and discuss ways the taxonomy can be used and further improved.

Abstract Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated different aspects of human values in software engineering, this mixed-methods study focuses on honesty as a critical human value. In particular, we studied (i) how to detect honesty violations in mobile apps, (ii) the types of honesty violations in mobile apps, and (iii) the perspectives of app developers on these detected honesty violations. We first develop and evaluate 7 machine learning (ML) models to automatically detect violations of the value of honesty in app reviews from an end-user perspective. The most promising was a Deep Neural Network model with F1 score of 0.921. We then conducted a manual analysis of 401 reviews containing honesty violations and characterised honesty violations in mobile apps into 10 categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. A developer survey and interview study with mobile developers then identified 7 key causes behind honesty violations in mobile apps and 8 strategies to avoid or fix such violations. The findings of our developer study also articulate the negative consequences that honesty violations might bring for businesses, developers, and users. Finally, the app developers’ feedback shows that our prototype ML-based models can have promising benefits in practice.

Failure to consider the characteristics, limitations, and abilities of diverse end-users during mobile app development may lead to problems for end-users, such as accessibility and usability issues. We refer to this class of problems ashuman-centric issues. Despite their importance, there is a limited understanding of the types of human-centric issues that are encountered by end-users and taken into account by the developers of mobile apps. In this paper, we examine what human-centric issues end-users report through Google App Store reviews, what human-centric issues are a topic of discussion for developers on GitHub, and whether end-users and developers discuss the same human-centric issues. We then investigate whether an automated tool might help detect such human-centric issues and whether developers would find such a tool useful. To do this, we conducted an empirical study by extracting and manually analysing a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub. Our analysis led to a taxonomy of human-centric issues that characterises human-centric issues into three-high level categories: App Usage, Inclusiveness, and User Reaction. We then developed machine learning and deep learning models that are promising in automatically identifying and classifying human-centric issues from app reviews and developer discussions. A survey of mobile app developers shows that the automated detection of human-centric issues has practical applications. Guided by our findings, we highlight some implications and possible future work to further understand and better incorporate addressing human-centric issues into mobile app development.

A growing number of software-intensive systems are being accused of violating or ignoring human values (e.g., privacy, inclusion, and social responsibility), and this poses great difficulties to individuals and society. Such violations often occur due to the solutions employed and decisions made by developers of such systems that are misaligned with user values. Stack Overflow is the most popular Q&A website among developers to share their issues, solutions (e.g., code snippets), and decisions during software development. We conducted an exploratory study to investigate the occurrence of human values violations in Stack Overflow posts. As comments under posts are often used to point out the possible issues and weaknesses of the posts, we analyzed 2,000 Stack Overflow comments and their corresponding posts (1,980 unique questions or answers) to identify the types of human values violations and the reactions of Stack Overflow users to such violations. Our study finds that 315 out of 2,000 comments contain concerns indicating their associated posts (313 unique posts) violate human values. Leveraging Schwartz’s theory of basic human values as the most widely used values model, we show that hedonism and benevolence are the most violated value categories. We also find the reaction of Stack Overflow commenters to perceived human values violations is very quick, yet the majority of posts (76.35%) accused of human values violation do not get downvoted at all. Finally, we find that the original posters rarely react to the concerns of potential human values violations by editing their posts. At the same time, they usually are receptive when responding to these comments in follow-up comments of their own.

eHealth technology is being developed at a rapid rate in line with advances in Information and Communication Technology (ICT). As eHealth technology increases, ensuring equitable accessibility and usability for all end-users is becoming a significant challenge. Considering human factors in the design phase of an application will help ensure usability and accessibility gaps are addressed. We conducted a survey with a broad range of users to investigate the relationship between different human factors and design elements that address these human factors. From the survey results, we developed a high-fidelity design prototype which we validated with a usability study to enhance the usability and accessibility for diverse end-users.

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects of such human value violations can be seen in various ways in society. In this work, we focus on the human value of honesty. We present a model to support the automatic identification of violations of the value of honesty from app reviews from an end-user perspective. Beyond the automatic detection of honesty violations by apps, we also aim to better understand different categories of honesty violations expressed by users in their app reviews. The result of our manual analysis of our honesty violations dataset shows that honesty violations can be characterised into ten categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. Based on these results, we argue for a conscious effort in developing more honest software artefacts including mobile apps, and the promotion of honesty as a key value in software development practices. Furthermore, we discuss the role of app distribution platforms as enforcers of ethical systems supporting human values, and highlight some proposed next steps for human values in software engineering (SE) research.

The elicitation of end-users& human values - such as freedom, honesty, transparency, etc - is important in the development of software systems. We carried out two preliminary Q-studies to understand (a) the general human value opinion types of eHealth applications (apps) end-users (b) the eHealth domain human value opinion types of eHealth apps end-users (c) whether there are differences between the general and eHealth domain opinion types. Our early results show three value opinion types using generic value instruments: (1) fun-loving, success-driven and independent end-user, (2) security-conscious, socially-concerned, and success-driven end-user, and (3) benevolent, success-driven, and conformist end-user. Our results also show two value opinion types using domain-specific value instruments: (1) security-conscious, reputable, and honest end-user, and (2) success-driven, reputable and pain-avoiding end-user. Given these results, consideration should be given to domain context in the design and application of values elicitation instruments.

Finding a parking space can be very stressful and time consuming. A variety of different vehicle parking applications have been developed but many fail to support diverse end-users. We captured diverse human-centric issues from user reviews and literature, and then created personas that encompass a wide representative range of parking app user groups. Using these personas, user stories were created, categorized and parking app tasks prioritized. We used these to develop a prototype new "smart parking app". A cognitive walk-through was employed using each of the personas and user stories to evaluate the app. With more human-centric factors taken into account in the design and development of the app, we found that majority of the human-centric frustrations identified were resolved, when compared with a commonly used parking app.

Information visualisation researchers have posited that author-driven narratives will allow information to be conveyed efficiently and argue for the adoption of storytelling techniques in information visualisation. However, there is limited work describing the effects of author-driven narratives in users' comprehension and memorability of visualisations in relation to interactive visualisations. Recommendations for author-driven visualisation stories are largely based on anecdotal reports or research from the arts, and not on studies in information visualisation. To investigate these issues, we carried out a study that compared purely author-driven narratives with interactive visualisations devoid of author narratives, in terms of comprehension and short-term and long-term memorability. We found that the presence of narration in author-driven stories significantly aided the understanding of information but had no significant effect on the long-term recall of information from visualisations.

Visual data storytelling has gained widespread adoption as a means of communicating information visualisation. This is partly due to the increased interest in data journalism. Besides being engaging, it has been shown to foster better comprehension and memorability of information to target audiences. The visual data story authoring process involves several stages. However, current tools neither consolidate the visual data story creation process nor integrate the essential features, such as the recommendation of logically sequenced story pieces, for producing coherent narratives. This paper briefly demonstrates our approach and framework for supporting the creation of logically sequenced visual data stories.

Effective visualisation plays a vital role in generating insights from data. The selection of graph types however, is highly dependent on the analysis tasks and data types at hand. For example, spatio-temporal visualisations encode changes in data over time and space. Although they have the potential of revealing overall tendencies and movement patterns, building effective spatio-temporal visualisations is challenging because it requires encoding all three attributes of spatio-temporal data i.e. thematic (values of attributes), temporal and spatial in a single visualisation. In this application design study, we present PedaViz for representing hour-level spatio-temporal attributes within a single visualisation; a 24-hour radial visual metaphor that encodes hour-level temporal and daily temperature attributes while utilising a thematic map display to present spatial attributes. The design was applied on city planning domain using Melbourne's pedestrian count and temperature data. Results of our preliminary user evaluation suggest that our visualisation is easily understandable by users; and supports users in carrying out selected analysis tasks.

An obstacle for users engaged in data analysis task is determining the most useful way to visually represent their particular datasets. For users with little background in data analytics, this is mostly done through a trial-and-error process. My focus is on investigating appropriate techniques for automatically generating visualisations that help in identifying inconsistencies in data; explicit modelling and visualisation of user hypotheses; and automatically generating and selecting visualisations that would help users accomplish data analysis tasks. The resulting platform would be a software prototype that would ingest data, understand user hypothesis, and then intelligently recommend dashboards with the best-suited visualisations to form a visual story-telling scenario.

We present a visualisation of Melbourne pedestrian count data and a visual metaphor for representing hour-level temporal dimension in this context. The pedestrian count data is captured from sensors located around the city. A visualisation web application is implemented that incorporates a thematic map of these sensor locations with a 24-hour clocklike polygon that shows pedestrian counts at every hour, and alongside a display of daily temperature. Our visualisation allows users to analyse how the city is used by pedestrians. Moreover, the design of our visualisation was driven by the type of analysis tasks carried out by city planners. The visualisation would help city planners better understand the dynamics of pedestrian activity within the city and aid them in urban management and design policy recommendation.