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84 results for “android”
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Demonstration of Android Jank when Fibonacci computation is done on main thread
<p>This video demonstrates Android Ui jank which occurs when heavy fibonacci computation is done on main thread</p>
Video demonstrating results of applying show view updates on Android Activity without Fibonacci Computation on the main thread
<p>Video demonstrating results of applying show view updates on Android Activity without Fibonacci Computation on the main thread</p>
Video demonstrating results of applying show view updates on Android Activity with Fibonacci Computation on the main thread
<p>Video demonstrating results of applying show view updates on Android Activity with Fibonacci Computation on the main thread</p>
Worrying confessions: A look at data safety labels on Android
<p>The Google Play Store recently introduced a data safety section in order to give users accessible insights into apps’ data collection practices. We analyzed the labels of 43,927 of the most popular apps. Almost one third of the apps with a label claims not to collect any data. But we also saw popular apps, including apps meant for children, admitting to collecting and sharing highly sensitive data like the user’s sexual orientation or health information for tracking and advertising purposes. To verify the declarations, we recorded the network traffic of 500 apps, finding more than one quarter of them transmitting tracking data not declared in their data safety label.</p> <p>This data set contains a dump of our database, including the top chart data and data safety labels from September 07, 2022, and the recorded network traffic.</p> <p>The analysis is available at our blog: <a href="https://www.datarequests.org/blog/android-data-safety-labels-analysis/">https://www.datarequests.org/blog/android-data-safety-labels-analysis/</a><br> The source code for the analysis is available on GitHub: <a href="https://github.com/datenanfragen/android-data-safety-label-analysis">https://github.com/datenanfragen/android-data-safety-label-analysis</a></p>
A thematic synthesis on the adoption of regression testing techniques in Android projects
<p>In software testing, employing regression techniques is a viable strategy to deal with the complexity and the constant evolution of applications since its primary goal is to ensure that changes made between versions do not change the system’s behavior. Although the literature has dedicated efforts to developing new regression testing techniques suitable for the Android mobile platform, studies are limited concerning demonstrating which techniques software developers employ in practice. This study aims to report on a thematic synthesis of adopting regression testing techniques in Android projects. The research encompassed four stages: (i) conducting a structured literature review on regression testing techniques for the Android platform, (ii) carrying out an expert survey, (iii) conducting interviews with industry professionals, and (iv) building a thematic synthesis. The thematic synthesis presented a model from analyzing the results obtained in this multimethod study on regression testing techniques. With such a stud, we could present empirical evidence on how professionals perform regression testing in Android projects, identify the commonly used regression testing techniques, and leverage the requirements for automating Android applications through regression testing.</p>
Android
The GOOGLE's mobile Operating System and of course, one of the most popular in this moment. https://en.wikipedia.org/wiki/Android_(operating_system) Source: Objaverse 1.0 / Sketchfab
DroidLeaks: A Large Collection of Resource Leak Bugs in Real-World Android Apps
<p>DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:<br> 1. the code repository of the app subject<br> 2. the concerned resource class<br> 3. the buggy code revision (and buggy file and method names)<br> 4. the bug-fixing code revision (i.e., link to the patch)<br> 5. the bug report or the corresponding pull request for patches (if located)</p>
The Real-World Benchmark of Android App Vulnerability from CVE
<p>The Real-World Benchmark of Android App Vulnerability from CVE named CVE-based benchmark.</p> <p>For more information about this benchmark, please refer to <a href="https://github.com/android-app-sast/VulsTotal">android-app-sast/VulsTotal: A Unified Platform for Evaluating SAST Tools for Android (github.com)</a></p>
Replication package for "How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?"
<p><strong>Replication Package</strong></p> <p>This section briefly describes the contents of the replication package, which makes it possible to replicate this study.</p> <p><strong>Dataset construction:</strong> The recreation of the GitLab AndroidTimeMachine instance is done using the snapshot related code found in the Kotlin program. This program uses a running instance of the AndroidTimeMachine’s Neo4j database and uses its data to make API calls to a running GitLab instance which imports the GitHub projects found. This step can be skipped, and a GitLab instance can be instantiated directly using our created Docker volumes. The is also capable of performing the Kotlin filtering steps. For this, it uses the results of the previous steps as input and clones each repository for performing the filtering procedure.</p> <p><strong>SLOC counting:</strong> SLOC counting related scripts are found in and implement our methods by cloning the project and running CLOC. It also plots the line graphs that we manually categorized into evolution trends. The raw SLOC data is found in , and all of the plotted evolution trends are found in.</p> <p><strong>Kotlin migration mining:</strong> The implementation for mining is located in , and implements our methods by cloning a repository from the running GitLab instance and executing described methods. All commits we found are located in.</p> <p><strong>Run-time efficiency:</strong> As mentioned in the thesis, we used AndroidRunner. A fork of AndroidRunner we used for conducting our experiments is located in . In the folder , each configuration combined with raw results is found per test subject. The final data we used after removing corrupt frame times is located in the folder, which also contains . This python script implements our statistical analysis methods.</p>
Dataset for the paper Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification
<p><strong>Short Description: </strong>This is the dataset for the paper "Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification", accepted for publication in the Journal of Systems and Software. </p> <p><strong>Link to this repository: </strong><a href="https://github.com/droidxp/paper-replication-package">https://github.com/droidxp/paper-replication-package</a></p> <p><strong>Authors of the Paper</strong></p> <ul> <li>Francisco Handrick da Costa</li> <li>Ismael Medeiros</li> <li>Thales Menezes</li> <li>João Victor da Silva</li> <li>Ingrid Lorraine da Silva</li> <li>Rodrigo Bonifácio</li> <li>Krishna Narasimhanb</li> <li>Márcio Ribeiro</li> </ul> <p><strong>Abstract</strong></p> <p>The popularization of the Android platform and the growing number of Android applications (apps) that manage sensitive data turned the Android ecosystem into an attractive target for malicious software. For this reason, researchers and practitioners have investigated new approaches to address Android's security issues, including techniques that leverage dynamic analysis to mine Android sandboxes. The mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app. This exploratory phase records all calls to sensitive APIs. Later, we can use this information to (a) prevent calls to other sensitive APIs (those not recorded in the exploratory phase) or (b) run the dynamic analysis tools again in a different version of the app. During this second execution of the fuzzing tools, a warning of possible malicious behavior is raised whenever the new version of the app calls a sensitive API not recorded in the exploratory phase.</p> <p>The use of a mining sandbox approach is an effective technique for Android malware analysis, as previous research works revealed. Particularly, existing reports present an accuracy of almost 70% in the identification of malicious behavior using dynamic analysis tools to mine android sandboxes. However, although the use of dynamic analysis for mining Android sandboxes has been investigated before, little is known about the potential benefits of combining static analysis with a mining sandbox approach for identifying malicious behavior. Accordingly, in this paper we present the results of two studies that investigate the impact of using static analysis to complement the performance of existing dynamic analysis tools tailored for mining Android sandboxes, in the task of identifying malicious behavior.</p> <p>In the first study we conduct a non-exact replication of a previous study (hereafter BLL-Study) that compares the performance of test case generation tools for mining Android sandboxes. Differently from the original work, here we isolate the effect of an independent static analysis component (DroidFax) they used to instrument the Android apps in their experiments. This decision was motivated by the fact that DroidFax could have influenced the efficacy of the dynamic analyses tools positively---through the execution of specific static analysis algorithms DroidFax also implements. In our second study, we carried out a new experiment to investigate the efficacy of taint analysis algorithms to complement the mining sandbox approach previously used to identify malicious behavior. To this end, we executed the FlowDroid tool to mine the source-sink flows from benign/malign pairs of Android apps used in previous research work.</p> <p>Our study brings several findings. For instance, the first study reveals that DroidFax alone (static analysis) can detect 43.75% of the malwares in the BLL-Study dataset, contributing substantially in the performance of the dynamic analysis tools in the BLL-Study. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, with a performance similar to that reached by dynamic analysis tools.</p> <p> </p>
Apps para dispositivos Android que generan Deepfakes
<p><em>Apps</em> para Android que generan <em>Deepfakes</em></p>
Smartphone osmTracker for Android survey in Villa Bolasco
<p>Tracks and waypoints of surveying path in Villa Bolasco and elements of interest (benches and trees)</p> <p> </p>
An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps
<p>This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps</strong>." This study has been accepted for publication at the 2023 International Conference on Technical Debt.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better manage this debt, developers may document their sub-optimal code as comments in the code (i.e., self-admitted technical debt or SATD). While prior research has investigated the occurrence and characteristics of SATD, this research has primarily focused on non-mobile systems. With millions of mobile applications (apps) in multiple genres available for end-users, there is a lack of research on sub-optimal code developers intentionally implement in mobile apps. </p> <p>In this study, we examine the occurrence and characteristics of SATD in 15,614 open-source Android apps. Our findings show that even though such apps contain occurrences of SATD, the volume per app (a median of 4) is lower than in non-mobile systems, with most debt categorized as Code Debt. Additionally, we identify typical elements in an app that are prone to intentional sub-optimal implementations. We envision our findings supporting researchers and tool vendors with building tools and techniques to support app developers with app maintenance. </p>
Hall-of-Apps: The Top Android Apps Metadata Archive
<p>The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present <strong>Hall-of-Apps</strong>, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: <a href="https://thesoftwaredesignlab.github.io/hall-of-apps-tools/">https://thesoftwaredesignlab.github.io/hall-of-apps-tools/</a></p>
"An Empirical Study of i18n Collateral Changes and Bugs in GUIs of Android apps" Complementary Material
<p>Study results for the paper: "An Empirical Study of i18n Collateral Changes and Bugs in GUIs of Android apps"</p>
The V2S Dataset: A Set of Android Screen Recordings, Training Images, and Models
<p>This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20)</p> <p>Link to V2S Paper: https://arxiv.org/abs/2005.09057</p>
Call Graph Soundness in Android Static Analysis
<div> <pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre> </div>
DEDICAT 6G Enhanced Experience Android streaming application delay measurements
<p>Performance measurements for the developed streaming application and comparison with the latest smartphones.</p>
Artifact for A Survey on Techniques to Profile the Energy Consumption of Android Applications
<p>To ensure availability and reproducibility, we have made all study artifacts publicly available for "<strong>A Survey on Techniques to Profile the Energy Consumption of Android Applications</strong>", a paper submitted to <strong><em>ACM Computing Surveys</em></strong>. In this study, we review state-of-the-art software-based energy profilers tailored for Android applications and propose a novel taxonomy for comparative analysis. We provide a brief overview of these energy profilers and compare their features. Based on the conducted literature review, we present conceptual architectures of the different types of software-based energy profilers to help build a tool that addresses the existing limitations in energy profiling.</p> <p>This repository contains the following files:</p> <ul> <li>Coding list generated from the Nvivo project (Files compared by number of codes). This file contains the codes used in taxonomy and the number of code references.</li> <li>Coding hierarchy generated from the Nvivo project (Codes compared by number of coding references). This file shows the hierarchy of codes and their share visually.</li> <li>List of selected papers (generated from the Nvivo project). This file presents the list of selected papers and metadata about these papers including publishing year, publisher, type of publication, etc.</li> <li>Search queries (SearchQueries). This file contains the search queries that are used to obtain the publications for review.</li> <li>Summary of the papers generated from the Nvivo project (Summary). This file contains the summary of the papers.</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.