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 software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS. OpenHarmony, a newly open-sourced mobile platform, has rarely been considered, although it is the one requiring the most attention as OpenHarmony is expected to occupy one-third of the market in China (if not in the world). To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony. Specifically, we start by presenting a literature review of mobile software engineering, attempting to understand what problems have been targeted by the mobile community and how they have been resolved. We then summarize the existing (limited) achievements of OpenHarmony and subsequently highlight the research gap between Android/iOS and OpenHarmony. This research gap eventually helps in forming the roadmap for conducting software engineering research for OpenHarmony.

Recognising empathy as vital in software engineering, this paper presents 17 actionable guidelines. Drawing on practitioners' experiences, it examines implementation challenges and strategies, and introduces a visual prioritization framework to support adoption and sustainable integration into everyday software engineering practice.

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 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 (Sort-then-Split by Groups), 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.

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With the huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has begun to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers' discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations, and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.

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.

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.

Context: User-Centered Design (UCD) advocates for high user engagement in the designing and developing of applications. Since applications are developed based on end-user expectations and address the context of use, UCD enhances the usability of the applications for the particular end users. However, UCD, with diverse end users, such as end users from low socioeconomic status, is challenging due to several reasons, such as communication, low digital literacy, and sensitivity. Objective: This study aims to investigate the application of User-Centered Design (UCD) with low socio-economic status end-users in order to identify the challenges that emerge in this context and derive recommendations for more inclusive future design initiatives. Methods: We followed a research through design approach. As part of the process, we surveyed 520 fisherfolk in Bangladesh, and conducted 16 focus group discussions to understand the context of use and to specify the requirements for several software applications, and conducted eight workshops for prototype development. Results: In this article, we present our experience of UCD with fisherfolk in Bangladesh, representing low socioeconomic status. We documented requirements for multiple software applications, developed personas to represent the end users, and iteratively designed two application prototypes through interactive workshops. Conclusion: Based on our experience, we recommend guidelines for future developers who intend to develop software with fisherfolk in Bangladesh, as well as for researchers and developers who plan to work with diverse end users.

The increasing reliance on mobile applications across various domains highlights the critical role of user reviews in shaping and guiding app development and improving user satisfaction. However, current app review systems, such as those used by the Apple App Store and Google Play, suffer from significant limitations, including the lack of structure and the proliferation of fake reviews. In this paper, we propose a structured review submission system that integrates predefined tags such as “Usability”, “User Experience” and “Features” and verification mechanisms such as “Verified Download” and “Verified Purchase” tags to enhance the authenticity and organisation of user feedback. We evaluate the system using a static prototype tested by 37 participants, gathering insights on usability and user satisfaction. Our findings demonstrate and highlight that the proposed structured system improves the clarity of reviews and enhances developer insights, while the verification tags increase trust in the authenticity of the feedback. Moreover, we integrate advanced Natural Language Processing (NLP) models like GPT-4 and RoBERTa further to further automate tag generation and sentiment analysis and to provide actionable insights for developers. Our study opens directions for improving mobile app review systems, with implications for user engagement, app quality, and developer responsiveness.

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.

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.

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.

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.

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 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: 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.

Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making pro cesses. However, deploying LLMs also presents diverse ethical challenges, and their mitigation strategies remain complex and domain-dependent. This paper aims to identify and categorise the key ethical concerns associated with using LLMs, examine existing mitigation strategies, and assess the outstanding challenges in implementing these strategies across various domains. We conducted a systematic mapping study, reviewing 39 studies that discuss ethical concerns and mitigation strategies related to LLMs. We analysed these ethical concerns using five ethical dimensions we extracted from various existing guidelines and frameworks, along with an analysis of mitigation strategies and implementation challenges. Our findings reveal that ethical concerns in LLMs are multi-dimensional and context-dependent. While proposed mitigation strategies address some of these concerns, significant challenges still remain. Our results highlight that ethical issues often hinder the practical implementa tion of mitigation strategies, particularly in high-stakes areas such as healthcare and public governance. Existing frameworks are often inflexible, failing to accommodate evolving societal expectations and diverse contexts.

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.

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on and refining prior API misuse taxonomies. Our evaluation of three widely used LLMs, StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot, reveals persistent challenges in API usage, particularly hallucination and intent misalignment. To address these issues, we propose Dr.Fix, an LLM-based automatic repair approach guided by our taxonomy. Dr.Fix improves repair accuracy compared to baseline prompting and existing repair methods, with gains of up to 38.4 BLEU and 40% in exact match on benchmark datasets. This work offers important insights into the current limitations of LLMs in API usage and provides insights into current limitations and points to directions for improving automated misuse repair in code generation systems.

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.

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.