Most programs today are written not by professional software developers, but by people with expertise in other domains working towards goals for which they need computational support. For example, a ...teacher might write a grading spreadsheet to save time grading, or an interaction designer might use an interface builder to test some user interface design ideas. Although these end-user programmers may not have the same goals as professional developers, they do face many of the same software engineering challenges, including understanding their requirements, as well as making decisions about design, reuse, integration, testing, and debugging. This article summarizes and classifies research on these activities, defining the area of End-User Software Engineering (EUSE) and related terminology. The article then discusses empirical research about end-user software engineering activities and the technologies designed to support them. The article also addresses several crosscutting issues in the design of EUSE tools, including the roles of risk, reward, and domain complexity, and self-efficacy in the design of EUSE tools and the potential of educating users about software engineering principles.
Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. Recent ...literature has encapsulated these cognitions in the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. Given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users' satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual's satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users' appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users' satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use-related outcomes.
To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine ...learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. The experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.
End‐user modeling of quality for web components Lizcano, David; Martínez‐Ortíz, Andrés‐Leonardo; López, Genoveva ...
Journal of software : evolution and process,
March 2023, 2023-03-00, 20230301, Volume:
35, Issue:
3
Journal Article
Peer reviewed
With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have ...imposed de facto standards for quality assessment in fast‐paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality‐in‐use metrics in order to define an absolute scale of comparison for web component quality.
‐Internet developers use an implicit quality model for Web Components
‐Web Components can be endowed with an explicit quality model based on ISO 25010
‐The relationship between implicit/explicit models can be validated by end‐users
‐Quality of web components can be predicted based in explicit metrics.
•People face self-driving cars with both fascination and reservation.•Openness towards self-driving cars correlates with both demographics and car use.•People are willing to ride in a self-driving ...car but are not yet ready to buy one.•Drivers do not trust self-drive technology to be safe and sophisticated enough.•Results suggest launching the technology gradually to increase both safety and trust.
In this paper we unpack and examine attitudes and potential barriers of end-users towards the self-driving car. We explore whether drivers have (mental) barriers and/or show resistance towards the self-driving car and, given such barriers and resistance are identified, investigate the main underlying reasons. Further, we suggest potential strategic implications for automotive companies and avenues to overcome, or at least mitigate, drivers’ barriers. The paper contributes to a better understanding of end-users’ opinions on radical innovations such as the self-driving car and strives to add value by linking scientific insights from both psychology as well as innovation literature. Only a limited number of studies so far have dealt with the potential barriers of users towards the self-driving car; therefore, it is our intent to provide first empirical evidence to trigger further research and foster a broader discussion on this relevant topic.
Studying human behavior is of particular interest within the field of Human-Computer Interaction (HCI) as it can provide insight into human performance. Prior HCI research suggests that mouse and ...keyboard monitoring may provide a more complete picture of user behavior under high cognitive loads like decision making and developing tasks. In this exploratory study we investigate the potential correlation between mouse behavioral patterns or keystroke dynamics and a set of End-User Development (EUD) behavioral attributes. We conduct a field test on 30 end-users interacting with a modern web-based EUD tool for the construction of simple web forms. Our findings reveal the existence of several significant correlations between end-users’ behavioral attributes and mouse pattern metrics or keystroke dynamics during the development process. Mouse pattern metrics like random and straight movements, mouse hovers, etc., can be associated with perceived ease use, perceived usefulness, self-efficacy, willingness to learn or risk-perception. Similarly, some keystroke dynamics like key press speed and down-to-down time can be associated with perceived ease of use or self-efficacy. The findings of this work show a new interesting research direction and may motivate the EUD research community to study further the end-users’ mouse and keyboard behavior in today's web-based EUD systems.
•A mouse and keyboard tracking exploratory study is conducted on a EUD environment.•Common mouse behavioral patterns are divided in different sets of mouse metrics.•The aim is to diagnose end-users’ behavioral states from mouse and keyboard input.•Mouse pattern metrics are correlated to all measured EUD behavioral attributes.•Some keystroke metrics are correlated to self-efficacy and perceived ease of use.
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows ...for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.
This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization ...has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.
PDF
