Google Chrome is a popular web browser developed by Google.
The Chrome User Experience Report is a public dataset of key user experience metrics for popular origins on the web, as experienced by Chrome users under real-world conditions.
https://bigquery.cloud.google.com/dataset/chrome-ux-report:all
For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/
License: CC BY 4.0
Photo by Edho Pratama on Unsplash
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License information was derived automatically
ABSTRACT
End-to-end (E2E) testing is a software validation approach that simulates realistic user scenarios throughout the entire workflow of an application. In the context of web
applications, E2E testing involves two activities: Graphic User Interface (GUI) testing, which simulates user interactions with the web app’s GUI through web browsers, and performance testing, which evaluates system workload handling. Despite its recognized importance in delivering high-quality web applications, the availability of large-scale datasets featuring real-world E2E web tests remains limited, hindering research in the field.
To address this gap, we present E2EGit, a comprehensive dataset of non-trivial open-source web projects collected on GitHub that adopt E2E testing. By analyzing over 5,000 web repositories across popular programming languages (JAVA, JAVASCRIPT, TYPESCRIPT, and PYTHON), we identified 472 repositories implementing 43,670 automated Web GUI tests with popular browser automation frameworks (SELENIUM, PLAYWRIGHT, CYPRESS, PUPPETEER), and 84 repositories that featured 271 automated performance tests implemented leveraging the most popular open-source tools (JMETER, LOCUST). Among these, 13 repositories implemented both types of testing for a total of 786 Web GUI tests and 61 performance tests.
DATASET DESCRIPTION
The dataset is provided as an SQLite database, whose structure is illustrated in Figure 3 (in the paper), which consists of five tables, each serving a specific purpose.
The repository table contains information on 1.5 million repositories collected using the SEART tool on May 4. It includes 34 fields detailing repository characteristics. The
non_trivial_repository table is a subset of the previous one, listing repositories that passed the two filtering stages described in the pipeline. For each repository, it specifies whether it is a web repository using JAVA, JAVASCRIPT, TYPESCRIPT, or PYTHON frameworks. A repository may use multiple frameworks, with corresponding fields (e.g., is web java) set to true, and the field web dependencies listing the detected web frameworks. For Web GUI testing, the dataset includes two additional tables; gui_testing_test _details, where each row represents a test file, providing the file path, the browser automation framework used, the test engine employed, and the number of tests implemented in the file. gui_testing_repo_details, aggregating data from the previous table at the repository level. Each of the 472 repositories has a row summarizing
the number of test files using frameworks like SELENIUM or PLAYWRIGHT, test engines like JUNIT, and the total number of tests identified. For performance testing, the performance_testing_test_details table contains 410 rows, one for each test identified. Each row includes the file path, whether the test uses JMETER or LOCUST, and extracted details such as the number of thread groups, concurrent users, and requests. Notably, some fields may be absent—for instance, if external files (e.g., CSVs defining workloads) were unavailable, or in the case of Locust tests, where parameters like duration and concurrent users are specified via the command line.
To cite this article refer to this citation:
@inproceedings{di2025e2egit,
title={E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects},
author={Di Meglio, Sergio and Starace, Luigi Libero Lucio and Pontillo, Valeria and Opdebeeck, Ruben and De Roover, Coen and Di Martino, Sergio},
booktitle={2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR)},
pages={10--15},
year={2025},
organization={IEEE/ACM}
}
This work has been partially supported by the Italian PNRR MUR project PE0000013-FAIR.
Which county has the most Facebook users?
There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.
Facebook – the most used social media
Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.
Facebook usage by device
As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.
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License information was derived automatically
Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Abstract: Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner's pervasiveness as they interrupt natural'' browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners. In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for cookie banner interaction.'' We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user's preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set---even for extensions that aim to implement the same cookie choice. Extensions forcookie banner interaction'' can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies. Abstract: Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner's pervasiveness as they interrupt
natural'' browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners. In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for cookie banner interaction.'' We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user's preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set---even for extensions that aim to implement the same cookie choice. Extensions forcookie banner interaction'' can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies. TechnicalRemarks: This repository hosts the dataset corresponding to the paper "A Large-Scale Study of Cookie Banner Interaction Tools and their Impact on Users’ Privacy", which was published at the Privacy Enhancing Technologies Symposium (PETS) in 2024.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Comparison data 6 for Neolamprologus multifasciatus.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Comparison data 1 for Neolamprologus multifasciatus.
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Google Chrome is a popular web browser developed by Google.
The Chrome User Experience Report is a public dataset of key user experience metrics for popular origins on the web, as experienced by Chrome users under real-world conditions.
https://bigquery.cloud.google.com/dataset/chrome-ux-report:all
For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/
License: CC BY 4.0
Photo by Edho Pratama on Unsplash