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29 results for “Android Apps”
Taxonomy of Security Weaknesses in Java and Kotlin Android Apps
<p>This is the replication package for the paper "Taxonomy of security weaknesses in Java and Kotlin Android apps" accepted for inclusion in The Journal of Systems & Software</p>
SAST database of repository Luca App Android
<p>SAST database as sqlite database containing the<a href="https://gitlab.com/lucaapp/android"> LucaApp Android Gitlab repository</a>.</p> <p>Retrieved on June 21<sup>nd</sup>, 2021 using our SAST analysis pipeline.</p>
Artefacts for ICSE 2021 technical paper: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"
<p>This repository represents our artefacts to replicate our paper which is in the proceedings of ICSE 2021: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"</p>
AndroCT: Ten Years of App Call Traces in Android
<p>A large-scale dataset on the dynamic profiles based on function calls of 35,974 benign and malicious Android apps from 10 historical years (2010 through 2019). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes dynamic profiles of each app resulting from the same-length of time (10 mins) of being exercised by randomly generated inputs on both emulator and real device, enabling interesting and useful app analysis that reason about app behaviors in an evolutionary perspective while informing the differences of app behaviors on different run-time hardware platforms. Since we have 20 yearly datasets associated with 35,974 unique Android apps across the 10 years, profiling these apps took 12,000 hours. Considering the costs of filtering out apps that were originally sampled but that we were unable to profile (due to various reasons such as broken APKs, not being executable because of incompatibility issues, not instrumentable, etc.), we took over two years to produce all these traces. We hope to save future researchers' time in producing such a set of dynamic data to enable their empirical and technical work. </p> <p>==================</p> <p>Thanks for your interest in our dataset. Collecting this dataset took tremendous computational and human effort. Thus, please observe the following restrictions in using our dataset: </p> <p> - Do not redistribute this dataset without our consent.<br> - Do not make commercial usage of this dataset.<br> - Get a faculty, or someone in a permanent position, to agree and commit to these conditions.<br> - When publishing your work that uses our dataset, please cite the following MSR 2021 data paper.</p> <p><br> @inproceedings{AndroidCT,<br> title = {AndroCT: Ten Years of App Call Traces in Android},<br> author = {<a href="https://2021.msrconf.org/profile/wenli">Wen Li</a>, <a href="https://2021.msrconf.org/profile/xiaoqinfu">Xiaoqin Fu</a>, and <a href="https://2021.msrconf.org/profile/haipengcai">Haipeng Cai</a>},<br> booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track},<br> year = {2021},<br> }</p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
iWanDroid: Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps
<p>The repo contains the tool iWanDroid: Artifacts for the paper Demand-driven Information Flow Analysis of WebView in Android Hybrid Apps, accepted at ISSRE 2023.</p> <p>Android hybrid apps augment native apps with web and inter-language communication capabilities. These apps facilitate the integration of web components, including JavaScript, into native apps. Besides, they allow a two-way communication where JavaScript can utilize functionality shared by the native side (Java). However, due to operational differences between Java and JavaScript, the semantics of this communication are complex. Tracking information flows via this communication channel, i.e., between these heterogeneous platforms, becomes intricate.</p> <p>Multiple approaches have been proposed to analyze hybrid apps. However, most of them focus on specific classes of web-induced vulnerabilities or provide rudimentary tracking of specific information flows via this communication channel. This work proposes a demand-driven analysis to comprehensively track information flow violations from the native side to JavaScript and vice-versa. To this end, our framework selectively creates data flow summaries of the shared native-side code based on its usage in the corresponding JavaScript code. We demonstrate the efficacy of our approach by applying it to various benchmarks and large-scale apps.</p> <p> </p>
Artifacts for Demystifying Device-specific Compatibility Issues in Android apps
<p>This artifact is for the paper "Demystifying Device-specific Compatibility Issues in Android Apps."</p> <p>We include our repositories link, commit hash, our GitHub crawler, its output, and some representative examples for Device-specific compatibility issues in this artifact.</p> <p>Please checkout the README.pdf first. The main artifact pack is the `device-specific-artifacts.zip`.</p> <p>Other zips starting with "repo-" are repositories archives.</p>
Twindroid - System calls traces - Android apps
<p>TwinDroid - a dataset of over 10 000 system calls traces, from both benign and infected Android apps. A large part of the dataset is composed of traces from pairs of benign and infected apps.</p> <p>For more traces you can visit https://zenodo.org/record/6465271#.Yls3F1yZNH4</p> <p>To trace Android application, you can use our script in https://github.com/RaphaelKhoury/automated-apk-tracing</p>
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>
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>
Apps para dispositivos Android que generan Deepfakes
<p><em>Apps</em> para Android que generan <em>Deepfakes</em></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>
Benchmark (Android apps) used for evaluating PermDroid
<p>Real-world Android Apps used in the evaluation part of an ISSTA paper "PermDroid: Automatically Testing Permission-Related Behaviour of Android Applications". </p>
Twindroid - System calls traces - Android apps - Extention
<p>TwinDroid - a dataset of system calls traces, from both benign and infected Android apps.</p> <p>For other traces you can visit https://zenodo.org/record/6464808#.Yls2yVyZNH4</p> <p>To trace apps, you can use our script in: https://github.com/RaphaelKhoury/automated-apk-tracing</p> <p> </p> <p> </p>
An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps: Appendix
<p>The appendix of our "An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps" paper.</p>
Towards Fine-granularity Malicious Component Detection for Android Apps
<p>These files are the experiment data of AMCDroid.</p> <p> (1) is the dataset used in the experiment (we do not use the VirusShare dataset in 2019 since we failed to download it);</p> <p> (2) is the malicious component detection result of the running example;</p> <p> (3) to (7) are the experiment data in RQ1 to RQ5;</p> <p> (8) is the malicious component detection result of Roaming Mantis; and</p> <p> (9) is the malicious component detection result in internal validity.</p> <p>We will open source the code of AMCDroid after our paper is accepted.</p>
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