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103 results for βMobile developmentβ
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−π‘β term contained in the j−π‘β user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
The Development of a Mobile-based App to Increase Uptake of Pre-Exposure Prophylaxis (PrEP) by Men Who Have Sex With Men in China
ClinicalTrials.gov study NCT04426656. IPD Sharing: YES. Countries: 1. Publications: 2.
Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development
<p>That video presents the paper entitled "Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development" accepted in SBES 2020 conference (Education track).</p>
Development of the multi-dimensional Mobility Divide Index as a methodology to assess the accessibility level of public transport systems
<p>This paper presents the development of a multi-dimensional Mobility Divide Index (MDI) for assessing the accessibility of public transport developed using a co-design approach, directly involving end-users in the index design process. The index measures the gap that persons with disabilities feel they need to over-come to use public transport in the same way non-disabled citizens do. The MDI covers six accessibility-related dimensions: 1) safety, 2) convenience, 3) comfort, 4) affordability, 5) travel time, 6) autonomy. The method paper describes the step-by-step approach to create the MDI as a set of indicators to be rated by people with different access needs to a) provide evidence of the main criticalities to be addressed through the design and implementation of new inclusive mobility solutions, b) guide the design of new inclusive mobility solutions and measure their impacts and c) inform the transport sector encouraging positive changes in transport by providing recommendations for policy-making, new directions for service innovation, improvements and practical advice or highlighting investment priorities to pave the way for a more inclusive mobility. We present our findings in ways that can inform universal design and provide actionable information to researchers, policymakers, transport and urban planners, operators and stakeholders’ representatives to promote inclusive and equitable mobility solutions for all. Finally, we suggest follow up research and innovation, as well as recommendations for its uptake and utilisation in the pursuit of European accessibility standards and requirements for products and services in the mobility sector. </p>
Developing a Mobile Method to Measure THC-induced Impairment
ClinicalTrials.gov study NCT03804840. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effect of the Mobile Application Developed for the Care of Sick Newborns
ClinicalTrials.gov study NCT05776615. IPD Sharing: NO. Countries: 1. Publications: 11.
Development of a Mobile Application to Promote Self-care in Patients With Fibromyalgia
ClinicalTrials.gov study NCT03004911. IPD Sharing: NO. Countries: 1. Publications: 11.
The Impact of a Mobile Application Designed for Adults at Risk of Developing Diabetes
ClinicalTrials.gov study NCT05592288. IPD Sharing: YES. Countries: 1. Publications: 6.
Development of a Rehabilitation Strengthening and Mobility Program for Ventilator Dependent Older Patients
ClinicalTrials.gov study NCT03195127. IPD Sharing: NO. Countries: 1. Publications: 1.
TAU Urban Acoustic Scenes 2020 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2020 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 64 hours of audio. Version 2 fixes synchronization between 118 segments from devices A, B, and C, and removes 5 incorrect audio segments.</p>
TAU Urban Acoustic Scenes 2022 Mobile, Development dataset
<p>TAU Urban Acoustic Scenes 2022 Mobile development dataset consists of 1-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 64 hours of audio. </p>
The dataset of the paper titled "Investigating End-Users' Values for Agriculture Mobile Applications Development: A Mixed-Methods Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and 10 main values with corresponding attributes and the referred names used in this paper submitted to IST.</p>
The dataset of the paper titled "Investigating End-Users' Values in Agriculture Mobile Applications Development: An Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, and 10 main values with corresponding attributes and the referred names used in this paper submitted to JSS.</p>
Supporting Developers in Addressing Human-centric Issues in Mobile Apps
<p>A replication package including a manually analysed dataset of 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, the results of our machine learning and deep learning approaches, our survey questions and raw responses from app developers to the survey.</p>
Data - Mobile phones and their impact on the socioeconomic development of rural women in Peru
<p>Project database Mobile phones and their impact on the socioeconomic development of rural women in Peru</p>
TUT Urban Acoustic Scenes 2018 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2018 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. Each acoustic scene has 864 segments (144 minutes of audio) recorded with device A (main device) and 72 segments of parallel audio (12 minutes) each recorded with devices B and C. The dataset contains in total 28 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2019 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. Each acoustic scene has 1440 segments (240 minutes of audio) recorded with device A (main device) and 108 segments of parallel audio (18 minutes) each recorded with devices B and C. The dataset contains in total 46 hours of audio.</p>
A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges
<p>This is the dataset for the paper: <strong>A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges</strong></p> <p>This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024) - Industry Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2408.09032">https://arxiv.org/abs/2408.09032</a></p> <p> </p> <h2><strong>Artifacts</strong></h2> <p>The table below describes the artifacts associated with this study.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Purpose</strong></td> </tr> <tr> <td>SurveyResponse.csv</td> <td>The results of the online survey. This file contains only responses from participants who completed all required survey questions.</td> </tr> <tr> <td>SurveyQuestions.pdf & SurveyQuestions.tex</td> <td>These files contain the survey questions in the form they were presented to participants. They are in PDF and LaTeX format.</td> </tr> <tr> <td>ParticipantLocation.xlsx </td> <td>This file shows the location (based on Qualtircs metadata) of participants who responded to the survey.</td> </tr> <tr> <td>IRB_Approval.pdf</td> <td>This is the IRB approval document.</td> </tr> <tr> <td>freetext-responses-categorized.xlsx</td> <td>This file is related to survey questions #20, #22, and #23. It is a manual categorization of free-text responses by the authors. </td> </tr> <tr> <td>likert-figures\*.pdf</td> <td>The files in this directory are the Likert figures used in the paper.</td> </tr> <tr> <td>multichoice-questions\*.csv</td> <td>The files in this directory show the frequency of occurrence for the answer options associated with single-choice and multi-choice questions.</td> </tr> <tr> <td>code\</td> <td>Python code to generate the Likert charts and the CSV files that show the frequency of occurrence for the answer options.</td> </tr> <tr> <td>LICENSE.txt</td> <td>Open-source license compliant with OSI.</td> </tr> </tbody> </table> <h2>Code</h2> <p>The code is written in Python. Below is a description of the files:</p> <ol> <li>requirements.txt - Required packages</li> <li>MultiChoiceAnalysis.py - This file contains the logic to analyze the multichoice questions to generate the frequency of occurrence of the answer options. The CSV files are created in the parent directory</li> <li>main-mobileSecurity.py - This file reads SurveyResponse.csv (in the parent directory) and has the code to generate the likert charts and calls MultiChoiceAnalysis.py. The Likert charts are created in the parent directory.</li> </ol> <p>Perform the below steps to execute the code:</p> <ol> <li> Create a Python virtual environment: <blockquote> python -m venv venv</blockquote> </li> <li>Activate the virtual environment: <blockquote> venv\Scripts\activate</blockquote> </li> <li>Install packages <blockquote> pip install -r requirements.txt</blockquote> </li> <li>Run the Python code: <blockquote> python main-mobileSecurity.py</blockquote> </li> </ol> <p>Below are details about the code execution output:</p> <ul> <li>The CSV and PDF files will be created in the parent directory.</li> <li>The Likert charts (i.e., PDF files) are created in a directory called "likert-figures"</li> <li>The CSV files are created in a directory called "multichoice-questions"</li> <li>Elements from the CSV files are part of the write-up in the Results section of the manuscript. The numeric digits that are part of the file name correspond to the survey question.</li> <li>The data present in Tables 2 to 5 in the manuscript is obtained from the CSV files generated by the Python code. Specifically: <ul> <li>Table 2 - \multichoice-questions\survey-11-frequency_single_choice.csv</li> <li>Table 3 - \multichoice-questions\survey-12-frequency_single_choice.csv</li> <li>Table 4 - \multichoice-questions\survey-13-frequency_single_choice.csv</li> <li>Table 5 - \multichoice-questions\survey-14-frequency_single_choice.csv and \multichoice-questions\survey-15-frequency_single_choice.csv</li> </ul> </li> </ul> <p><em>Note: We include these output files in the artifacts package by default. The user can delete these directories and run the code to regenerate the files.</em></p>
Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions
<p>This is the dataset for the paper: Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions</p> <p>This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2409.07945">https://arxiv.org/abs/2409.07945</a></p>
Development of a Mobile Heath Augmented Brief Suicide Prevention Intervention for People With SMI
ClinicalTrials.gov study NCT03198364. IPD Sharing: NO. Countries: 1. Publications: 3.
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