Using R Software Research Articles

How can feature usage be tracked across product variants? Implicit Feedback in Software Product Lines

Implicit feedback involves gathering information about software usage to grasp how and when the software is utilized. This study investigates the integration of implicit feedback mechanisms into Software Product Line Engineering (SPLE). While common in product-based development for identifying bugs, usability issues, and informing requirement prioritization, its adoption in SPLs has been limited due to the unique challenges posed by SPLE, such as the emphasis on Domain Engineering over Application Engineering, and the need for systematic reuse of shared assets. We propose a novel approach to incorporate feedback practices into Domain Engineering, thereby shifting the focus from individual product variants to the SPL platform, and specifically moving from product-based feedback to feature-based feedback. Based on a case study, we suggest that product derivation includes a second step that injects the trackers at the time of derivation, using a Feedback Model that complements the Configuration Model for feedback analysis.To test this approach, we introduce FEACKER, an extension to pure::variants as the variability manager. FEACKER injects trackers when the product variant is derived. The findings are validated through a Technology Acceptance Model (TAM) evaluation and a focus group discussion, providing insights into the feasibility, acceptance, and potential impact of platform-based feedback in SPLE. The results indicate agreement on the benefits of conducting feedback analysis at the platform level and the perception that FEACKER seamlessly extends the capabilities of pure::variants.

An Integrated Multi-Criteria Decision Analysis and Structural Equation Modeling Application for the Attributes Influencing the Customer’s Satisfaction and Trust in E-Commerce Applications

Since the COVID-19 pandemic has forced most industries to embrace an online platform utilizing technological breakthroughs, it has significantly impacted our daily lives. Businesses that use marketplaces to sell and trade products to customers while increasing their participation through online shopping or e-commerce are among the sectors that take advantage of these situations. The current study set out to evaluate the level of customer satisfaction, trust, and service quality of the e-commerce application to enhance the system and provide a better shopping experience. Facebook, Shopee, Lazada, Shein, and TikTok were the five e-commerce platforms evaluated. The Philippines was the location of this study, and at least 200 people answered the survey, which was conducted in-person and online and consisted of 72 questionnaires. The researchers assessed twelve latent variables: perceived security, customer satisfaction, application interface, brand equity, tangibility, reliability, responsiveness, assurance, empathy, and information credibility. Structural equation modeling and multi-criteria decision analysis were used to analyze the data. The findings demonstrated that assurance, application interface, information credibility, and brand equity directly impacted service quality. Moreover, a direct and significant correlation exists between customer satisfaction and service quality. Customers’ trust is significantly impacted by their level of satisfaction and perception of security. The e-commerce apps were ranked using a multi-criteria decision analysis technique, which is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on tangibility, responsiveness, assurance, reliability, and empathy. Facebook received a low rating, whereas Shopee was ranked highest. To further enhance the services offered, the lowest rated application may use the results from the combination of the TOPSIS and SEM results. Additionally, application providers, managers, and researchers examining the user–software interaction of relevant e-commerce applications might utilize the study’s results to enhance their services regarding the purchasing experience and provide a sustainable service. Finally, this study is among the first to use the structural modeling approach to evaluate customer trust and satisfaction while integrating service quality and TOPSIS.

Facilitating and automating usability testing of educational technologies

AbstractUsability evaluation is a key element to ensure a positive user experience with any software and it is especially important in educational software tools where there are many different actors involved (lecturers, students, administrators, etc.). However, evaluating usability is not an easy task for nonexpert evaluators. To facilitate this evaluation task, this article presents a Methodology for Usability Testing (MUT) and a system (CALMUT) that assists nonexpert evaluators in the application of the methodology by automatizing the calculations and facilitating their interpretation. This can be very useful for learning and instructional designers but also to people involved in the decision of introducing or not a new educational software. To develop the proposal, a literature review of different usability metrics, methods, and systems was carried out first, followed by a selection and adaptation for novice usability evaluators. This article also presents a case study where lecturers tested the usability of an educational software following the proposal and shows that using MUT and CALMUT helps people without previous experience detect the main usability problems of educational systems before deciding whether to use them or not.

Testing the dominant mediator in EPPM: An empirical study on household anti-malware software users

A key research area in information systems security (ISec) is explaining or improving users’ IS security outcomes via the extended parallel process model (EPPM) lens. While the theoretical construct in emotional valence (e.g., fear) and cognitive valence (e.g., perceived efficacy) were deemed as mediators in previous EPPM-related ISec studies, existing research has ignored the value of testing and reporting the dominant mediator between the emotional valence and the cognitive valence. In this paper, we reintroduce the theoretical origins of the dominant mediator assumption in EPPM and highlight its merits using the multiple mediation method. Theoretically, we illustrate how testing and reporting the dominant mediator can help identify the dominant mechanism triggering specific behavioral outcomes. Further, this paper questions the dominant mediating role of fear on the behavioral outcome in ISec context. Methodologically, this study proposes to assess the dominant mediator via a multiple mediation model instead of using the discriminant value equation introduced by Witte (1995), Witte et al. (1996) and enhanced by Chen et al. (2021) when testing the EPPM theory in the ISec context.

PMBLDC motor (PMBLDCM) with non-linear controller usage in electric propulsion of electric vehicles to control rotor speed (RS) and torque (EMT)

In present days existing vehicles are replaced with electric vehicles (EVs) with usage of PMBLDCM motors because of simplicity in design, rough usage, responding speed, and high efficiency. Due to the usage of dynamic loads in electric vehicles with PMBLDCM motor controlling speed and electromagnetic torque were major problems. These problems along with other problems faced in electric propulsion system (EPS) of electric vehicles (EVs) can be solved with the usage of the proposed non-linear controller (ANF-SMC) along with the usage of PMBLDCM motor for speed regulation and fuzzy parameter tuning based on motor performance under variable speed conditions. The proposed controller is designed by the usage of simulation software along fuzzy logic. To validate the proposed non-linear control system performance results compared with others exhibiting one linear and three non-linear controllers. Simulation results proved that with various loaded conditions proposed system performs well in all aspects when compared to four existing controllers.

Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.

A Hybrid Physical-Digital Simulation Laboratory to Expedite Context-Aware Design and Usability Testing in Digital Health.

Fit within existing physical and digitalised workflows is a critical aspect of digital health software usability. Early, iterative exploration of contextual usability issues is complicated by barriers of access to healthcare settings. The Validitron SimLab is a new facility for digital health prototyping that augments immersive, realistic physical environments with a digital sandbox allowing new and existing software to be easily set up and tested in the physical space.

Considerations for End-User Development in the Caregiving Domain

As service robots become more capable of autonomous behaviors, it becomes increasingly important to consider how people will be able to communicate with a robot about what task it should perform and how to do the task. There has been a rise in attention to end-user development (EUD), where researchers create interfaces that enable non-roboticist end users to script tasks for autonomous robots to perform. Currently, state-of-the-art interfaces are largely constrained, often through simplified domains or restrictive end-user interaction. Motivated by our past qualitative design work exploring how to integrate a care robot in an assisted living community, we discuss challenges of EUD in this complex domain. One set of challenges stems from different user-facing representations, e.g., certain tasks may lend themselves better to a rule-based trigger-action representations, whereas other tasks may be easier to specify via a sequence of actions. The other stems from considering the needs of multiple stakeholders, e.g., caregivers and residents of the facility may all create tasks for the robot, but the robot may not be able to share information about all tasks with all residents due to privacy concerns. We present scenarios that illustrate these challenges and also discuss possible solutions.

A Review of IoT Firmware Vulnerabilities and Auditing Techniques.

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.

Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being “forgetful” of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications.

Introduction to the modeling of complex chemical reaction equilibrium using gPROMS® and GAMS®

AbstractIn the present paper several aspects of the simulation of complex chemical equilibrium are discussed, using two research and educational software: gPROMS® and GAMS®. All computations are performed to boost the understanding of chemical engineering students, undergraduate, and graduate, of the complex concepts behind both thermo‐chemical processes and optimization techniques. Four case studies with varying levels of difficulty illustrate the proposed methodology and robustness of the modeling systems: Catalytic steam reforming of bio‐ethanol at high temperatures. Partial oxidation of naphthalene using either air or pure oxygen. Supercritical water gasification of Microalga Spirulina. Dry and mixed reforming of glucose.Our results indicate that the use of optimization tools offered bygPROMS® and GAMS®software are useful and efficient tools to calculate the chemical and phase equilibrium by minimizing the Gibbs energy, provided that adequate initial guesses are used. Furthermore, the computational time spent in the calculations was generally quite short. The obtained equilibrium compositions are benchmarked using the ubiquitous process simulators: ASPEN‐Plus® and/or ASPEN‐Hysys® as well as data available from the open literature. We obtained excellent agreement between our results and their counterpart obtained using ASPEN‐Plus®. In the last part of this manuscript, authors share their experience in supervising senior term projects on the subject. A questionnaire was used to collect qualitative and quantitative feedback from the nine students involved in the projects. Their responses to a questionnaire focused on the chemical engineering thermodynamics theory as well as software usage during their investigations. This questionnaire demonstrates that students can overcome mathematical complexity by either using mathematical software or equivalent modeling systems (e.g., Matlab®, Mathematica©, gPROMS®, and GAMS®).

Development and usability testing of a patient digital twin for critical care education: a mixed methods study.

Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care. A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey. Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies. A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.

DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles

Evaluation of chemical drug-likeness is essential for the discovery of high-quality drug candidates while avoiding unwarranted biological and clinical trial costs. A high-quality drug candidate should have promising drug-like properties, including pharmacological activity, suitable physicochemical and ADMET properties. Hence, in silico prediction of chemical drug-likeness has been proposed while being a challenging task. Although several prediction models have been developed to assess chemical drug-likeness, they have such drawbacks as sample dependence and poor interpretability. In this study, we developed a novel strategy, named DBPP-Predictor, to predict chemical drug-likeness based on property profile representation by integrating physicochemical and ADMET properties. The results demonstrated that DBPP-Predictor exhibited considerable generalization capability with AUC (area under the curve) values from 0.817 to 0.913 on external validation sets. In terms of application feasibility analysis, the results indicated that DBPP-Predictor not only demonstrated consistent and reasonable scoring performance on different data sets, but also was able to guide structural optimization. Moreover, it offered a new drug-likeness assessment perspective, without significant linear correlation with existing methods. We also developed a free standalone software for users to make drug-likeness prediction and property profile visualization for their compounds of interest. In summary, our DBPP-Predictor provided a valuable tool for the prediction of chemical drug-likeness, helping to identify appropriate drug candidates for further development.

Contradiction in text review and apps rating: prediction using textual features and transfer learning.

Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user's experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users' reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

Rworkflows: automating reproducible practices for the R community

Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has been revolutionary, GitHub does not have any default built-in checks for minimal coding standards or software usability. This makes it difficult to assess the current quality R packages, or to consistently use them over time and across platforms. While GitHub-native solutions are technically possible, they require considerable time and expertise for each developer to write, implement, and maintain. To address this, we develop rworkflows; a suite of tools to make robust continuous integration and deployment (https://github.com/neurogenomics/rworkflows). rworkflows can be implemented by developers of all skill levels using a one-time R function call which has both sensible defaults and extensive options for customisation. Once implemented, any updates to the GitHub repository automatically trigger parallel workflows that install all software dependencies, run code checks, generate a dedicated documentation website, and deploy a publicly accessible containerised environment. By making the rworkflows suite free, automated, and simple to use, we aim to promote widespread adoption of reproducible practices across a continually growing R community.

Measuring End-user Developers’ Episodic Experience of a Low-code Development Platform. A Preliminary Study

Background: As low-code development platforms (LCDPs) are becoming a trend, understanding how end-user developers think and feel as they work with such platforms is important. Particularly, assessing experiences during episodes of use can contribute to overall experience throughout long-term use. Aim: This paper aims to understand end-user developers’ episodic experience when they are building an application on a low-code platform and to provide guidance on how such experiences can be measured. Method: We designed the Episodic Developer Experience Questionnaire for LCDPs based on prior literature and refined it through expert Delphi sessions. The instrument contains 10 individual experience items, capturing various aspects of episodic experience. We further validated it through remote online tests on an LCDP. Results: The results showed significant differences in the relationships between items describing aspects of overall experience and items describing perceptions of tool quality and task difficulty. Programming expertise also affected end-user developers’ episodic experience. Conclusion: The study illustrates the design of questionnaire-based experience assessment in the context of development and identifies the importance of separating personal experience from assessment of tasks and tools since tool quality and task difficulty do not necessarily influence experience straightforwardly.

DIY Assistive Software: End-User Programming for Personalized Assistive Technology

Existing assistive technologies often fail to support the unique and personalized needs of blind and visually impaired (BVI) people. Thus, BVI people have become domain experts in customizing and 'hacking' assistive technology in order to creatively suit their needs. However, current assistive technologies are difficult to hack and alter at the individual level due to the skill and resources required. Due to these challenges, new approaches to supporting Do-It-Yourself (DIY) technology creation are needed to make applying the DIY concept to high-tech assistive technologies for BVI people more feasible. Through my dissertation, I aim to better support DIY assistive software design and development, so that BVI people can design, create, and customize assistive software for themselves and their communities. To accomplish this, I plan to address three primary research goals. First, through qualitative interviews, I present an understanding of why and how BVI people engage in assistive technology customization and adaptation, and the challenges and opportunities that arise from this practice. Second, I aim to investigate how end-user programming can be applied as an approach to DIY assistive technology creation, by designing, building, and evaluating a prototype end-user programming tool. Finally, I aim to understand and improve the real-world feasibility of end-user programming in this domain, by designing tools to improve the accessibility of debugging visual technologies. By addressing these primary research questions, my planned dissertation contributes to the HCI and accessibility community's understanding of DIY assistive software, accessible end-user programming, and accessible debugging techniques. By supporting BVI in creating and modifying their assistive software, this research aims to promote the democratization of technology creation and support BVI people in having greater control over AI-based technologies in their lives.

Decoding Success: Assessing the Online Pseudocode Interpreter through Users Insights

Pseudocode is vital in enhancing programming comprehension, making it an essential component of the Programming Logic Formulation (CS1) course. However, many CS1 students from various senior high school strands struggle with this concept, highlighting the need for additional learning resources. This research evaluated a pseudocode interpreter designed for first-year Information and Communications Technology (ICT) students at Iloilo Science and Technology University in Iloilo City, Philippines. Using random sampling, 147 first-year ICT students were selected to evaluate the software's user experience in usability, credibility, appearance, and loyalty using the Standardized User Experience Percentile Rank Questionnaire (SUPR-Q). Likewise, five ICT experts were selected using purposive sampling to evaluate the system's validity as instructional material based on its content and on the material's organization, form, and style using the university's Quality Form for Instructional Materials (IM). The user experience evaluation yielded positive results, with students unanimously agreeing on the software's ease of use, dependability, and user-friendliness. The expert evaluation confirmed the system's validity as instructional material, commending its content, organization, form, and style. These findings suggest that the pseudocode interpreter provides immediate benefits for students, offering a valuable learning tool. Moreover, the software has the potential to be adopted by other faculty members, promoting a more efficient and captivating learning experience. Future research could investigate how interpreters affect student learning and their usefulness in various educational settings.

Design and usability evaluation of a web-based pitch control training app for transgender women.

Transgender people often experience dysphoria because the way their voice is perceived does not match their gender identity. Such dysphoria negatively affects mental health and quality of life, and is particularly an issue in trans women. Dysphoria can be reduced via gender-affirming voice and communication training provided by human experts, but the accessibility of such training is often limited. As a supplement or alternative to human-guided training, our team has thus developed an early prototype of voice training software for transfeminine users (i.e., trans women and nonbinary users who were assigned male at birth). The software is accessible via a web browser and provides three vocal pitch exercises together with real-time feedback about the user's pitch relative to a desired target pitch curve. This paper presents the main technical features and results of a single-session usability evaluation with 5 transfeminine participants. We further present future plans for expansion to other exercises and voice aspects (particularly resonance) as well as plans for clinical trials.

Fog Computing in Optically Networked Space Constellations

This article provides an overview of computing models to describe the frontier in space-based computing, and describes the definitions and ecosystem historically termed Cloud and Edge. It is proposed that optically internetworked, Proliferated Low-Earth Orbit (PLEO) space-based computing should be considered Fog computing, not Cloud nor Edge. Space-based computing should orient toward software paradigms observed in Fog computing such as internetworked, secure, reactive services. Definitions are provided, with comparisons between Cloud, Fog, Edge, and near-term space-based computing. Implications and the ecosystem around space-based computing are discussed. Specifically, improper application of Edge or Cloud computing models to space systems causes misrepresentations and can have real consequences to investments and technology development. To call such systems Edge is to under-represent their capabilities, and suggests a misleading limitation of resources and unreliability of networks. To call such systems Cloud is to over-represent their capabilities, and incorrectly suggest an ease of deployment and operation of commercially-available distributed computing technologies in space. Acceptance of Fog computing as the model for on-orbit processing will advance usage of reactive service-based software instead of less scalable architectures. Unresolved trends and technical risks to fruition of Fog computing in space are also presented.