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.

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 4,163 app reviews from the Google Play Store and identified 1,429 age-related reviews and 2,734 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 1,429 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.

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.

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.

Model-driven approaches are increasingly used in different domains, such as education, finance and app development, in order to involve non-developers in the software development process. Such tools are hugely dependent on visual elements and thus might not be accessible for users with specific challenges, e.g., visual impairments. To locate and analyse existing literature on the accessibility of low-code approaches, their strengths and weaknesses and key directions for future research. We carried out a systematic literature review and searched through five leading databases for primary studies. We used both quantitative and qualitative methods for data synthesis. After reviewing and filtering 918 located studies, and conducting both backward and forward snowballing, we identified 38 primary studies that were included in our analysis. We found most papers focusing on accessibility of visual languages and block-based programming. Limited work has been done on improving low code programming environment accessibility. The findings of this systematic literature review will assist researchers and developers in understanding the accessibility issues in low-code approaches and what has been done so far to develop accessible approaches.

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. 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. 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. 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. 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. • Crowdsourced Software Engineering task recommendation is a trending research area. • Content-based approaches dominate existing software task recommendation systems. • Current recommendation systems in this field lack integration of human factors.

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.

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.

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.

The Internet of Things (IoT) has led to a surge in smart devices, generating vast volumes of data. Cloud computing offers scalability but does not suffice for many real-time and privacy-sensitive IoT applications. This limitation has prompted a blend of both edge and cloud resources, creating the need for seamless integration, known as the “compute continuum“. Testing applications and resource management techniques within this continuum is vital but can be very complex. Simulation and emulation are preferred methods, with emulation providing more accurate representations of real-world environments. In this paper, we introduce iContinuum, a novel emulation toolkit facilitating an intent-based platform for edge-to-cloud testing and experimentation. Leveraging Software-Defined Networking (SDN) and containerization, iContinuum enables experimentation and performance evaluation while aligning application requirements with actual performance. We present our detailed architecture, implementation, and evaluation of iContinuum, showcasing how our proposed toolkit bridges the gap between simulation and real-world deployment within compute continuum environments, and further demonstrate the effectiveness of Intent-Based Scheduling through a specific use case.

Human-Centered e-Health Development: An Accessible Visual Modeling Tool

Machine learning (ML) components are used heavily in many current software systems, but developing them responsibly in practice remains challenging. 'Responsible ML' refers to developing, deploying and maintaining ML-based systems that adhere to human-centric requirements, such as fairness, privacy, transparency, safety, accessibility, and human values. Meeting these requirements is essential for maintaining public trust and ensuring the success of ML-based systems. However, as changes are likely in production environments and requirements often evolve, design-time quality assurance practices are insufficient to ensure such systems' responsible behavior. Runtime monitoring approaches for ML-based systems can potentially offer valuable solutions to address this problem. Many currently available ML monitoring solutions overlook human-centric requirements due to a lack of awareness and tool support, the complexity of monitoring human-centric requirements, and the effort required to develop and manage monitors for changing requirements. We believe that many of these challenges can be addressed by model-driven engineering. In this new ideas paper, we present an initial meta-model, model-driven approach, and proof of concept prototype for runtime monitoring of human-centric requirements violations, thereby ensuring responsible ML behavior. We discuss our prototype, current limitations and propose some directions for future work.

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.

Context: Human-centric software design and development prioritises the way users prefer to complete their jobs, rather than expecting users to adapt to the software. Software users can have different genders, ages, cultures, languages, disabilities, socioeconomic statuses, and educational backgrounds, among many other differences. Due to the inherently varied nature of these differences and their impact on software usage, preferences and issues of users can vary, resulting in user-specific defects that we term as ‘human-centric defects’ (HCDs). Objective: This research aims to understand the perception and current management practices of such HCDs by software practitioners, identify key challenges in reporting, understanding and fixing them, and provide recommendations to improve HCDs management in software engineering. Methods: We conducted a survey and interviews with software engineering practitioners to gauge their knowledge and experience on HCDs and the defect tracking process. Results: We analysed fifty (50) survey- and ten (10) interview-responses from SE practitioners and identified that there are multiple gaps in the current management of HCDs in software engineering practice. There is a lack of awareness regarding human-centric aspects, causing them to be lost or under-appreciated during software development. Our results revealed that handling HCDs could be improved by following a better feedback process with end-users, a more descriptive taxonomy, and suitable automation. Conclusion: HCDs, given their diverse end-user base, present a major challenge to software practitioners. In the software engineering domain, research on HCDs has been limited and requires effort from research and practice communities to create awareness and support for human-centric aspects.

Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to increasingly leverage their data to extract meaningful insights and enhance business profitability. ML components enable predictive capabilities, anomaly detection, recommendation, accurate image and text processing, and informed decision-making. However, developing systems with ML components is not trivial; it requires time, effort, knowledge, and expertise in ML, data processing, and software engineering. There have been several studies on the use of model-driven engineering (MDE) techniques to address these challenges when developing traditional software and cyber–physical systems. Recently, there has been a growing interest in applying MDE for systems with ML components. The goal of this study is to further explore the promising intersection of MDE with ML (MDE4ML) through a systematic literature review (SLR). Through this SLR, we wanted to analyze existing studies, including their motivations, MDE solutions, evaluation techniques, key benefits and limitations. Our SLR is conducted following the well-established guidelines by Kitchenham. We started by devising a protocol and systematically searching seven databases, which resulted in 3,934 papers. After iterative filtering, we selected 46 highly relevant primary studies for data extraction, synthesis, and reporting. We analyzed selected studies with respect to several areas of interest and identified the following: 1) the key motivations behind using MDE4ML; 2) a variety of MDE solutions applied, such as modeling languages, model transformations, tool support, targeted ML aspects, contributions and more; 3) the evaluation techniques and metrics used; and 4) the limitations and directions for future work. We also discuss the gaps in existing literature and provide recommendations for future research. This SLR highlights current trends, gaps and future research directions in the field of MDE4ML, benefiting both researchers and practitioners.

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.

Android apps often have a “dark mode” option used in low-light situations, for those who find the conventional color palette problematic, or because of personal preferences. Typically developers add a dark mode option for their apps with different backgrounds, text, and sometimes iconic forms. We wanted to understand the actual provision of this dark mode in real-world Android apps through an empirical study of posts from Stack Overflow and real-world Android app analysis. Using these approaches, we identified the aspects of dark mode that developers implemented as well as the key difficulties they experienced in implementing it. We performed a quantitative analysis using open-coding of more than 300 discussion threads to create a taxonomy regarding the aspects discussed by developers with respect to dark mode in Android. Our quantitative analysis of over 6,000 Android apps highlights which dark mode features are typically provided in Android apps and which aspects developers care about during dark mode design. We also examined four app development support tools to see how well they aid Android app development for dark mode. From our analysis, we distilled some key lessons to guide further research and actions in aiding developers with supporting users who require such assistive features. For example, developers should be aware of the potential risks in using unsuitable dark mode design schema and researchers should take dark mode features into consideration when developing app development support tools.

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.

Extracting Queryable Knowledge Graphs from User Stories: An Empirical Evaluation

Mobile software apps ("apps") are one of the prevailing digital technologies that our modern life heavily depends on. A key issue in the development of apps is how to design gender-inclusive apps. Apps that do not consider gender inclusion, diversity, and equality in their design can create barriers (e.g., excluding some of the users because of their gender) for their diverse users. While there have been some efforts to develop gender-inclusive apps, a lack of deep understanding regarding user perspectives on gender may prevent app developers and owners from identifying issues related to gender and proposing solutions for improvement. Users express many different opinions about apps in their reviews, from sharing their experiences, and reporting bugs, to requesting new features. In this study, we aim at unpacking gender discussions about apps from the user perspective by analysing app reviews. We first develop and evaluate several Machine Learning (ML) and Deep Learning (DL) classifiers that automatically detect gender reviews (i.e., reviews that contain discussions about gender). We apply our ML and DL classifiers on a manually constructed dataset of 1,440 app reviews from the Google App Store, composing 620 gender reviews and 820 non-gender reviews. Our best classifier achieves an F1-score of 90.77%. Second, our qualitative analysis of a randomly selected 388 out of 620 gender reviews shows that gender discussions in app reviews revolve around six topics: App Features, Appearance, Content, Company Policy and Censorship, Advertisement, and Community. Finally, we provide some practical implications and recommendations for developing gender-inclusive apps.

The rising prevalence of chronic diseases necessitates effective self-management strategies. mHealth interventions have shown promise in supporting self-management, but their under-utilization remains a challenge. Individuals with chronic diseases exhibit significant variations in their conditions, severity levels, and associated complications, highlighting the need for more tailored approaches. Adaptive User Interfaces (AUIs) can be used as a solution to address the diverse and dynamic needs of individuals with chronic diseases. We have created an AUI prototype based on existing literature, incorporating presentation, content, and behaviour adaptation. Our user study employs a mixed-method research approach to gather insights from users by interacting with our prototype. The future plans of the study aim to utilise insights obtained from the data analysis to automatically generate AUIs using a model-driven approach.

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.

The predicted increase in demand for data-intensive solution development is driving the need for software, data, and domain experts to effectively collaborate in multi-disciplinary data-intensive software teams (MDSTs). We conducted a socio-technical grounded theory study through interviews with 24 practitioners in MDSTs to better understand the challenges these teams face when delivering data-intensive software solutions. The interviews provided perspectives across different types of roles including domain, data and software experts, and covered different organisational levels from team members, team managers to executive leaders. We found that the key concern for these teams is dealing with data-related challenges. In this article, we present a theory of dealing with data challenges that explains thechallengesfaced by MDSTs including gaining access to data, aligning data, understanding data, and resolving data quality issues; thecontextin andconditionunder which these challenges occur, thecausesthat lead to the challenges, and the relatedconsequencessuch as having to conduct remediation activities, inability to achieve expected outcomes and lack of trust in the delivered solutions. We also identifiedcontingenciesor strategies applied to address the challenges including high-level strategic approaches such as implementing data governance, implementing new tools and techniques such as data quality visualisation and monitoring tools, as well as building stronger teams by focusing on people dynamics, communication skill development and cross-skilling. Our findings have direct implications for practitioners and researchers to better understand the landscape of data challenges and how to deal with them.

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.

There has been a very large growth in interest in big data analytics to discover patterns and insights. A major challenge in this domain is the need to combine domain knowledge – what the data means (semantics) and what it is used for – with advanced data analytics and visualization techniques to mine and communicate important information from the huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics applications. However, delivering such big data analytics applications requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillsets usually take a long time to build and have high running costs. An alternative is to provide domain experts and data scientists – the end users – with tools they can use to create and deploy complex data analytics application solutions directly with less technical skills required. In this paper we present a survey and analysis of several current research and practice approaches to supporting data analytics for end-users, identifying key strengths, weaknesses and opportunities for future research.

Taking into account the diverse human aspects - gender, age, emotions, personality, language, culture, physical and mental challenges, etc - is critical towards achieving more human-centric design of software systems. Human-centric aspects affecting software have long been underestimated or even ignored as a result of the lack of in-depth capture and understanding during development. The use of technology has become the norm and the range of users has increased from just adults to children as well as seniors. Modelling frameworks are methods to represent the way a software system should be defined, and to date, little research has been done on age-related issues within modelling frameworks. In this paper, we investigate how human-centric aspects regarding age can be better modelled by extending these modelling frameworks. We introduce an extension to wireframe-based designs so that they can cater for decisions regarding age within the modelling framework. We have evaluated this modelling extension using multiple questionnaires as well as usability testing by using the extended age-modelling wireframe approach to design a news app. Questionnaires were used to evaluate the requirements of the users and developers for the extended wireframes. Our analysis shows that when using our extended wireframes, developers can cater for different user types and their accessibility needs easily and therefore users can use the prototypes with more ease.

Classifying mobile apps based on their description is beneficial for several purposes. However, many app descriptions do not reflect app functionalities, whether accidentally or on purpose. Most importantly, these app classification methods do not work if the app description is unavailable. This paper investigates a Reverse Engineering-based Approach to Classify mobile apps using The data that exists in the app, called REACT. To validate the proposed REACT method, we use a large set of Android apps (24,652 apps in total). We also show REACTs' extendibility for malware/anomaly detection and prove its reliability and scalability. However, our analysis shows some limitations in REACT procedure and implementation, especially for similar feature based app grouping. We discuss the root cause of these failures, our key lessons learned, and some future enhancement ideas. We also share our REACT tools and reproduced datasets for the app market analyst, mobile app developers and software engineering research communities for further research purposes.

Health-related mobile applications are known as eHealth apps. These apps make people more aware of their health, help during critical situations, provide home-based disease management, and monitor/support personalized care through sensing/interaction. eHealth app usage is rapidly increasing with a large number of new apps being developed. Unfortunately, many eHealth apps do not successfully adopt Human-Centric Issues (HCI) in the app development process and its deployment stages, leading them to become ineffective and not inclusive of diverse end-users. This paper provides an initial assessment of key human factors related to eHealth apps by literature review, existing guidelines analysis, and user studies. Preliminary results suggest that Usability, Accessibility, Reliability, Versatility, and User Experience are essential HCIs for eHealth apps, and need further attention from researchers and practitioners. Therefore, outcomes of this research will look to amend support for users, developers, and stakeholders of eHealth apps in the form of improved actionable guidelines, best practice examples, and evaluation techniques. The research also aims to trial the proposed solutions on real-world projects.

Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q&A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.

Many current software systems suffer from a lack of consideration of the human differences between end users. This includes age, gender, language, culture, emotions, personality, education, physical and mental challenges, and so on. We describe our work looking to consider these characteristics by incorporation of human centric-issues throughout the model-driven engineering process lifecycle. We propose the use of the co-creational "living lab" model to better collect human-centric issues in the software requirements. We focus on modelling these human-centric factors using domain-specific visual languages, themselves humancentric modelling artefacts. We describe work to incorporate these human-centric issues into model-driven engineering design models, and to support both code generation and run-time adaptation to different user human factors. We discuss continuous evaluation of such human-centric issues in the produced software and feedback of user reported defects to requirements and model refinement.

We present BiDaML (Big Data Analytics Modeling Languages), an integrated suite of visual languages and supporting tool to help end-users with the engineering of big data analytics solutions. BiDaML, our visual notations suite, comprises six diagrammatic notations: brainstorming diagram, process diagram, technique diagrams, data diagrams, output diagrams and deployment diagram. BiDaML tool provides a platform for efficiently producing BiDaML visual models and facilitating their design, creation, code generation and integration with other tools. To demonstrate the utility of BiDaML, we illustrate our approach with a realworld example of traffic data analysis. We evaluate BiDaML using two types of evaluations, the physics of notations and a cognitive walkthrough with several target end-users e.g. data scientists and software engineers.

Big data and analytics are increasingly used in different domains to gain insights and to improve decision-making. Developing big data analytics solutions is a complex task involving multidisciplinary teams and users - with no data science and programming background - to professional data scientists and software engineers. Different stakeholders work with a variety of data types, tasks and concepts in different languages from high- level domain concepts to low level programming languages and technical concepts. In order to advance the level of abstraction beyond low-level data analysis technical details, we demonstrate our BiDaML tool. BiDaML brings all stakeholders around one tool to specify, model and document their big data applications using a novel set of domain-specific visual languages (DSVLs).

Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.

Online social networks make it more convenient for people to find and communicate with other people based on shared interests, ideas, association with different groups, etc. Common social networks such as Facebook and Twitter have hundreds of millions or even billions of users scattered all around the world sharing interconnected data. Users demand low latency access to not only their own data but also their friends' data, often very large, e.g. videos, pictures etc. However, social network service providers have a limited monetary capital to store every piece of data everywhere to minimise users' data access latency. Geo-distributed cloud services with virtually unlimited capabilities are suitable for large scale social networks data storage in different geographical locations. Key problems including how to optimally store and replicate these huge datasets and how to distribute the requests to different datacenters are addressed in this paper. A novel genetic algorithm-based approach is used to find a near-optimal number of replicas for every user's data and a near-optimal placement of replicas to minimise monetary cost while satisfying latency requirements for all users. Experiments on a Facebook dataset demonstrate our technique's effectiveness in outperforming other representative placement and replication strategies.

In this paper, a hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing. The weight sharing ability of convolutional neural networks (CNNs) is considered as a level of hierarchy in these networks. Weight sharing reduces the number of free parameters and improves the generalization ability. In the proposed structure, a small CNN which is used for feature extractor is shared between the whole input image pixels. A scalable architecture for implementing extensive CNNs is resulted using a smaller and modularized trainable network to solve a large and complicated task. The proposed structure causes less training time, fewer numbers of parameters and higher test data accuracy. The recognition accuracy for recognizing unseen data shows improvement in generalization. Also presented are application examples for face recognition. The comprehensive experiments completed on ORL, Yale and JAFFE face databases show improved classification rates and reduced training time and network parameters.