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.

Open-source software (OSS) development relies on effective collaboration among distributed contributors. Yet, current OSS project recommendation systems primarily emphasize technical attributes, overlooking the collaboration and community aspects that influence contributors' decisions to join and remain in projects. This study investigates how team dynamics within OSS communities influence project selection and how these preferences vary across contributors' motivations. We conducted an online survey with 198 OSS practitioners, combining quantitative and qualitative analyses to capture contributors' perceptions of team dynamics. The results reveal that communication-related team dynamics such as responsiveness, tone, and clarity of replies are consistently prioritized across practitioners. However, the relative importance of these team dynamics differs according to contributors' motivations. For instance, practitioners motivated by gaining reputation or networking preferred inclusive project communities that encouraged diverse participation. These findings highlight that understanding how team dynamics align with contributors' motivations provides valuable insights into practitioners' project selection behaviour. Those insights can inform the design of future human-aware project recommendation systems that better account for social collaboration quality and motivational fit.

In recent years, mobile applications have become indispensable tools for managing various aspects of life. From enhancing productivity to providing personalized entertainment, mobile apps have revolutionized people’s daily routines. Despite this rapid growth and popularity, gaps remain in how these apps address the needs of users from different age groups. Users of varying ages face distinct challenges when interacting with mobile apps, from younger users dealing with inappropriate content to older users having difficulty with usability due to age-related vision and cognition impairments. Although there have been initiatives to create age-inclusive apps, a limited understanding of user perspectives on age-related issues may hinder developers from recognizing specific challenges and implementing effective solutions. In this study, we explore age discussions in app reviews to gain insights into how mobile apps should cater to users across different age groups. We manually curated a dataset of 4163 app reviews from the Google Play Store and identified 1429 age-related reviews and 2734 non-age-related reviews. We employed eight machine learning, deep learning, and large language models to automatically detect age discussions, with RoBERTa performing the best, achieving a precision of 92.46%. Additionally, a qualitative analysis of the 1429 age-related reviews uncovers six dominant themes reflecting user concerns: Age Appropriateness of Content, Language and Recommendations, Age Verification and Access Barriers, Usability and Accessibility Across Ages, Privacy and Safety Concerns, Interactions and Relationships and Recommendations and Feature Requests. Our findings reveal that users frequently encounter inappropriate content for children, struggle with strict or error-prone age verification systems, and emphasize the need for age-friendly accessibility and safety features. To address these issues, we offer actionable recommendations for app developers, including implementing flexible or gradient-based age restrictions, prioritizing safety features in kids’ apps, and strengthening parental controls.

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.

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.

Open source software (OSS) development continues to expand, yet software practitioners often struggle to select suitable projects, leading to inefficient onboarding and disengagement. Understanding how contributors select OSS projects is important for supporting contributors onboarding, engagement, and long-term participation within OSS communities. This study investigates contributors' project-selection preferences in OSS projects and examines how these preferences correlate with contributors' motivations and demographic backgrounds. Through an online survey of 208 practitioners, we found that demographic factors, such as age, gender, and the OSS role they held, significantly correlate with their motivations. Additionally, preferences for project characteristics such as project age, development stage, and documentation quality vary based on specific motivations. Importantly, our findings are presented through a comparative lens, analyzing the responses of newcomers to OSS and experienced OSS practitioners separately to uncover their distinct preferences. Lastly, we explore software practitioners' perspectives on how existing recommendation systems could better support project selection and align with their motivations. By disentangling the unique needs of newcomers to OSS and OSS practitioners, our findings provide insights for researchers, OSS project owners, and software practitioners to improve contributor onboarding, engagement, and retention, while also informing future project recommendation systems and improving the OSS ecosystem.

Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating contexts, that degrade model performance are a common occurrence in production settings. Monitoring enables early detection and mitigation of these runtime issues, helping maintain users' trust and prevent unwanted consequences for organizations. Aim: This study aims to provide a comprehensive overview of the ML monitoring literature. Method: We conducted a multivocal literature review (MLR) following the well established guidelines by Garousi to investigate various aspects of ML monitoring approaches in 136 papers. Results: We analyzed selected studies based on four key areas: (1) the motivations, goals, and context; (2) the monitored aspects, specific techniques, metrics, and tools; (3) the contributions and benefits; and (4) the current limitations. We also discuss several insights found in the studies, their implications, and recommendations for future research and practice. Conclusion: Our MLR identifies and summarizes ML monitoring practices and gaps, emphasizing similarities and disconnects between formal and gray literature. Our study is valuable for both academics and practitioners, as it helps select appropriate solutions, highlights limitations in current approaches, and provides future directions for research and tool development.

GitHub and Jira projects typically contain many issues and issue comments used to track project tasks and defects. An important class of issues that needs appropriate consideration is called “human-centric issues”. These issues relate to different human characteristics of end users that need to be identified, tracked and managed differently from traditional technical-related issues. Current management of these human-centric issues during defect management is limited. We introduce a novel dashboard – the (Human-centric Issue Visualiser – HCIV) that categorises and tags these HCIss. We built HCIV prototypes for the two platforms, GitHub and Jira. These tag issues and present them in various visual forms to software practitioners. Using the dashboard, human-centric issues can be prioritised and tracked, and machine learning-generated classifications can be overridden. To reflect these interactions, associated GitHub and Jira issue tags are updated while the user interacts with our dashboard. The user evaluations of our dashboard prototypes show their potential for human-centric issue management. A demo of the GitHub version of the tool being used can be viewed at https://youtu.be/v49aiRiDIPs , and the Jira version can be viewed at https://youtu.be/qQM72SErmqs .

Low-code applications are gaining popularity across various fields, enabling non-developers to participate in the software development process. However, due to the strong reliance on graphical user interfaces, they may unintentionally exclude users with visual impairments, such as color blindness and low vision. This paper investigates the accessibility issues users report when using low-code applications. We construct a comprehensive dataset of low-code application reviews, consisting of accessibility-related reviews and non-accessibility-related reviews. We then design and implement a complex model to identify whether a review contains an accessibility-related issue, combining two state-of-the-art Transformers-based models and a traditional keyword-based system. Our proposed hybrid model achieves an accuracy and F1-score of 78% in detecting accessibility-related issues.

Fairness is one of the socio-technical concerns that must be addressed in software systems. Considering the popularity of mobile software applications (apps) among a wide range of individuals worldwide, mobile apps with unfair behaviors and outcomes can affect a significant proportion of the global population, potentially more than any other type of software system. Users express a wide range of socio-technical concerns in mobile app reviews. This research aims to investigate fairness concerns raised in mobile app reviews. Our research focuses on AI-based mobile app reviews as the chance of unfair behaviors and outcomes in AI-based mobile apps may be higher than in non-AI-based apps. To this end, we first manually constructed a ground-truth dataset, including 1,132 fairness and 1,473 non-fairness reviews. Leveraging the ground-truth dataset, we developed and evaluated a set of machine learning and deep learning models that distinguish fairness reviews from non-fairness reviews. Our experiments show that our best-performing model can detect fairness reviews with a precision of 94%. We then applied the best-performing model on approximately 9.5M reviews collected from 108 AI-based apps and identified around 92K fairness reviews. Next, applying the K-means clustering technique to the 92K fairness reviews, followed by manual analysis, led to the identification of six distinct types of fairness concerns (e.g., ‘receiving different quality of features and services in different platforms and devices’ and ‘lack of transparency and fairness in dealing with user-generated content’). Finally, the manual analysis of 2,248 app owners’ responses to the fairness reviews identified six root causes (e.g., ‘copyright issues’) that app owners report to justify fairness concerns.

Context: Crowdsourced Software Engineering (CSE) offers outsourcing work to software practitioners by leveraging a global online workforce. However, these software practitioners struggle to identify suitable tasks due to the variety of options available. Hence, there have been a growing number of studies on introducing recommendation systems to recommend CSE tasks to software practitioners. Objective: The goal of this study is to analyze the existing CSE task recommendation systems, investigating their extracted data, recommendation methods, key advantages and limitations, recommended task types, the use of human factors in recommendations, popular platforms, and features used to make recommendations. Methods: This SLR was conducted according to the Kitchenham and Charters’ guidelines. We used manual and automatic search strategies without putting any time limitation for searching the relevant papers. Results: We selected 65 primary studies for data extraction, analysis, and synthesis based on our predefined inclusion and exclusion criteria. Based on our data analysis results, we classified the extracted information into four categories according to the data acquisition sources: Software Practitioner’s Profile, Task or Project, Previous Contributions, and Direct Data Collection. We also organized the proposed recommendation systems into a taxonomy and identified key advantages, such as increased performance, accuracy, and optimized solutions. In addition, we identified the limitations of these systems, such as inadequate or biased recommendations and lack of generalizability. Our results revealed that human factors play a major role in CSE task recommendation. Further, we identified five popular task types recommended, popular platforms, and their features used in task recommendation. We also provided recommendations for future research directions. Conclusion: This SLR provides insights into current trends, gaps, and future research directions in CSE task recommendation systems such as the need for comprehensive evaluation, standardized evaluation metrics, and benchmarking in future studies, transferring knowledge from other platforms to address cold start problem.

Given the inherent non-deterministic nature of machine learning (ML) systems, their behavior in production environments can lead to unforeseen and potentially dangerous outcomes. For a timely detection of unwanted behavior and to prevent organizations from financial and reputational damage, monitoring these systems is essential. This paper explores the strategies, challenges, and improvement opportunities for monitoring ML systems from the practitioners perspective. We conducted a global survey of 91 ML practitioners to collect diverse insights into current monitoring practices for ML systems. We aim to complement existing research through our qualitative and quantitative analyses, focusing on prevalent runtime issues, industrial monitoring and mitigation practices, key challenges, and desired enhancements in future monitoring tools. Our findings reveal that practitioners frequently struggle with runtime issues related to declining model performance, exceeding latency, and security violations. While most prefer automated monitoring for its increased efficiency, many still rely on manual approaches due to the complexity or lack of appropriate automation solutions. Practitioners report that the initial setup and configuration of monitoring tools is often complicated and challenging, particularly when integrating with ML systems and setting alert thresholds. Moreover, practitioners find that monitoring adds extra workload, strains resources, and causes alert fatigue. The desired improvements from the practitioners perspective are: automated generation and deployment of monitors, improved support for performance and fairness monitoring, and recommendations for resolving runtime issues. These insights offer valuable guidance for the future development of ML monitoring tools that are better aligned with practitioners needs.

Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.

Context: There is an increase in the investment and development of data-intensive (DI) solutions, systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD). Delivery of DI solutions requires a multidisciplinary skill set, but there is limited knowledge about how multidisciplinary teams develop DI systems and manage TD. Objective: This research contributes empirical, practice based insights about multidisciplinary DI team TD management practices. Method: This research was conducted as an exploratory observation case study. We used socio-technical grounded theory (STGT) for data analysis to develop concepts and categories that articulate TD and TDs debt management practices. Results: We identify TD that the DI team deals with, in particular technical data components debt and pipeline debt. We explain how the team manages the TD, assesses TD, what TD treatments they consider and how they implement TD treatments to fit sprint capacity constraints. Conclusion: We align our findings to existing TD and TDM taxonomies, discuss their implications and highlight the need for new implementation patterns and tool support for multidisciplinary DI teams.

Mobile health (mHealth) applications are increasingly adopted for chronic disease management, yet they face persistent challenges related to accessibility, inclusivity, and sustained engagement. Patients' needs evolve dynamically with their health progression, adherence, and caregiver support, creating unique requirements engineering (RE) challenges that traditional approaches often overlook. This study introduces ChroniUXMag, a framework for eliciting and analysing inclusivity requirements in mHealth design. Building on InclusiveMag and GenderMag principles, the framework aims to help researchers and practitioners systematically capture and evaluate factors that influence how individuals with chronic conditions perceive, trust, and interact with mHealth systems. The framework was developed through two stages of the InclusiveMag process. In the first stage, inclusivity facets were identified through a systematic literature review, focus groups, interviews, and a large-scale survey. In the second stage, these facets were synthesised into personas representing diverse health situations, attitudes, and digital practices, and integrated into an adapted cognitive walkthrough form. Thirteen facets were identified that capture the socio-technical complexity of mHealth use, including trust, digital literacy, dependency, and cultural context. These facets support structured, persona-driven evaluations that reveal inclusivity barriers often missed by traditional usability assessments. ChroniUXMag contributes to RE by offering a reproducible, evidence-based approach for embedding inclusivity into mHealth requirements. Future work will extend the third stage Apply through practitioner-led evaluation in real-world design contexts.

eHealth apps are mobile apps that help in self-management of critical illnesses, provide home-based disease management, and assist with personalized care through education, sensing, and interaction. Users of eHealth apps are naturally very diverse in terms of their human aspects, e.g., their emotional reactions to the apps, varying language proficiency, socioeconomic status, educational level, cognitive style, physical and mental challenges, gender, age, and personality. Unfortunately, many eHealth apps do not take these user differences sufficiently into account, making them ineffective or even unusable. This article presents our enhanced and actionable guidelines developed to better support human aspects in mobile eHealth apps. Some of these guidelines are specific, such as collecting minimal personal data or requirements, while others are more generic, applicable specifically to eHealth apps. We discuss how key human aspects, such as usability, accessibility, reliability, and validity, as well as diverse user issues can be addressed in practice with real-life eHealth app examples. We then collected feedback from expert mobile app developers, software engineers, and other relevant eHealth app stakeholders to assess the usefulness and applicability of the proposed guidelines and to identify areas where further refinement and development are needed.

mHealth interventions hold promise for supporting the self-management of chronic diseases, yet their limited utilisation remains a problem. Given the significant variability among individuals with chronic diseases, tailored approaches are imperative. Adaptive User Interfaces (AUIs) may help to address the diverse and evolving needs of this demographic. To investigate this approach, we developed an AUI prototype informed by existing literature and used it as the basis for a focus group and interview study involving 22 participants. Concurrently, a quantitative survey was carried out to extract preferences for AUIs in chronic disease related applications with 90 participants. Our findings reveal that user engagement with AUIs is influenced by individual capabilities and disease severity. Additionally, we explore user preferences for AUIs, expanding the adaptation literature by uncovering usage challenges, proposing practical strategies for enhanced AUI design, and acknowledging potential trade-offs between usability and adaptation. Lastly, we present design considerations for AUIs in chronic disease applications, aiming to prevent user overload and maintain critical software functionality and usability aspects.

eHealth apps are mobile apps that help in self-management of critical illnesses, provide home-based disease management, and help with personalized care. Users of eHealth apps are naturally very diverse in terms of their human aspects, e.g., their age, gender, emotional reactions to the apps, cognitive style, physical and mental challenges. Unfortunately, many eHealth apps do not take these user differences sufficiently into account, making them ineffective or even unusable. This paper reports a study from eHealth app stakeholders' – developers and end-users – perspectives on critical challenges and benefits of better incorporating human aspects into eHealth app development and usage. We also investigate how different human aspects are being addressed by developers, which ones are the most important for different user groups, and which ones are currently missing/poorly handled. A mixed-method approach that integrates qualitative and quantitative research was used for this study. We gathered and analyzed data from 240 online survey responses and 25 detailed interviews within the same study and validated the results. We report key issues encountered in eHealth app design, difficulty in addressing different human aspects, areas requiring further research and practical assistance, and recommend our findings to best address these challenges. We found addressing human aspects throughout the app development life-cycle is beneficial for more effective eHealth apps. Our findings also suggest the need for improved standards and guidelines, better developer-user collaborative culture, and better human aspects education to produce more effective eHealth apps. This paper investigates current approaches used in the eHealth app domain that take into account the human aspects of app users. The paper guides eHealth app stakeholders, future researchers, academia and industry partners be aware of human aspects related challenges and improve produce apps.