As of 2024, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 62 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

According to a survey conducted between late 2021 and early 2022, JavaScript is the most used programming language worldwide, with 56 percent of respondents reporting that they use the language. Python was the second most used language at 50.7 percent.

According to the survey, the size of the JavaScript programming language community is roughly **** percent of software developers as of 2023, making it the most popular programming language in the world. Python is also a popular community for programmers, with **** percent of developers.

The most popular programming language used in the past 12 months by software developers worldwide is JavaScript as of 2024, according to ** percent of the software developers surveyed. This is followed by Python at ** percent of the respondents surveyed.

Data Statement for CommitBench

EXECUTIVE SUMMARY

CURATION RATIONALE

DOCUMENTATION FOR SOURCE DATASETS

LANGUAGE VARIETIES

SPEAKER DEMOGRAPHIC

ANNOTATOR DEMOGRAPHIC

SPEECH SITUATION AND CHARACTERISTICS

PREPROCESSING AND DATA FORMATTING

CAPTURE QUALITY

LIMITATIONS

METADATA

DISCLOSURES AND ETHICAL REVIEW

ABOUT THIS DOCUMENT

This dataset can be found at this link. If you download and extract it, its size will be 50 GB! To make it easier to use, I've uploaded it here.

Explanation of fields in user entry:

1. login: Username, the registered name of the user, which cannot be changed after login
2. name: Nickname, the exhibited name of the user, which can be changed
3. email: the public email address of the user
4. id: the user ID allocated by GitHub
5. bio: self description
6. blog: the URL of the user’s public blog
7. company: working unit
8. hirable: if the user is looking for a job
9. location: the public location of the user
10. type: if this is a personal, an organization, or a bot account
11. created_at: the timestamp of the account creation
12. updated_at: the timestamp of the latest change of the user information
13. is_suspicious: if this is a suspicious account
14. followers: Number of followers
15. following: Number of followings
16. commits: Number of commits
17. public_gists: Number of public gists
18. public_repos: Number of public repositories
19. follower_list: the ID list of users who are following her
20. following_list: the ID list of users who are following her
21. commit_list: the list of commit operations she conducted a) commit_at: commit_at, the timestamp that the committer submit the code changes b) generate_at: generate_at, the timestamp that the author commit the code changes c) committer_id: ID of the committer d) author_id: ID of the author e) message: Content of message added f) repo_name: Name of the committed repository g) repo_id: ID of the committed repository h) repo_description: Description of the committed repository i) repo_owner_id: ID of the owner of the committed repository
22. repo_list: the list of repositories she created or forked a) full_name: Full name b) id: the repository ID allocated by GitHub c) description: Description d) size: The size e) license: The license f) stargazers_count: The number of stars received g) fork: Whether this is a forked or an originally created repository h) owner_id: ID of the owner of the repository i) created_at: the timestamp of the repository creation j) pushed_at: the timestamp of the last push operation to this repository k) updated_at: the timestamp of the latest change of this repository l) has_wiki: if the repository has a wiki document m) open_issues: the number of the open issues of the repository n) language: programming language o) forks_count: the number of forks of of this repository on GitHub p) default_branch: the branch of this repository

File Citation Gong, Qingyuan; Zhang, Jiayun; Chen, Yang; Xiao, Yu; Fu, Xiaoming; Hui, Pan; Li, Xiang; Wang, Xin, 2018, "README.txt", A Representative User-centric Dataset of 10 Million GitHub Developers, https://doi.org/10.7910/DVN/T6ZRJT/5MJZAZ, Harvard Dataverse, V1

Dataset Citation Gong, Qingyuan; Zhang, Jiayun; Chen, Yang; Xiao, Yu; Fu, Xiaoming; Hui, Pan; Li, Xiang; Wang, Xin, 2018, "A Representative User-centric Dataset of 10 Million GitHub Developers", https://doi.org/10.7910/DVN/T6ZRJT, Harvard Dataverse, V1

Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which—and to what extent—these concepts are included in these integrated experiences, especially when compared to concepts commonly taught in introductory computer science courses. Thus, it is unclear how integrated computing activities serve the goal of broadening participation in computing. To address this deficit, we compiled a database of 81 integrated computing activities, constructed a framework of fundamental programming concepts, and scored each activity in the database for the presence of each concept. The dataset also includes different activity features, including discipline, programming language, student age, and duration of activity. Methods Selection Criteria: Features and Limitations Non-CS Disciplinary Learning Objectives The first selection criterion for activities to include in the analysis was the inclusion of learning objectives in a discipline other than computing. No restrictions were placed on which other disciplines qualified, and we found activities from language arts, math, science, art, music, foreign language, history, social studies, and even spatial skill development for young children. One indirect benefit of requiring non-computing disciplinary learning objectives was that many included activities have substantive lesson plans. These lesson plans make the activities more accessible to teachers by including TPACK-related information, such as disciplinary learning objectives for the activity. As a result, the authors recognize the limitations of requiring non-computing learning objectives but also that it provides a level of authenticity and accessibility for the included activities. One of the major sources of computing integration activities affected by this requirement was the ScratchEd website. Scratch is a popular language for computing integration activities, aided by an extensive repository of student- and teacher-created projects that users are encouraged to remix into their own projects. The thousands of programs in this repository are of widely varying complexity and quality, and most of them are listed with a topic but without explicit learning objectives. To draw from this wealth of activities without comprehensively including projects, we identified lists of vetted computing integrated activities using Scratch to include in the analysis. These lists were "Integrated Scratch Programming in the Curriculum," "Scratch Projects Across the Curriculum," "From Music to Math: Scratch Across Every Subject," and "Scratch Cross-Curricular Integration Guide." Similarly, resources related to the Snap! language had plentiful examples of projects across disciplines with limited explicit non-CS disciplinary learning objectives. Block-Based Programming Languages Because computing integration activities are becoming popular, an initial search revealed too many activities to score in one analysis. To narrow the scope of the analysis, the next selection criterion was that the activity had to use a block-based programming language. This criterion has benefits and limitations. One of the main benefits for the goal of the current analysis was that block-based activities include a range of concepts, regardless of their syntactic or semantic difficulty (Grover & Basu, 2017; Papadakis et al., 2014). This benefit means that concepts that best serve the activity can be included for learners with little to no programming experience (Weintrop & Wilensky, 2018). The associated limitation, however, was that concepts are also restricted by the blocks that are built into the language. Most popular languages use a low-floor, high-ceiling design that includes blocks for all concepts that would be taught in an introductory programming course, though (Grover, 2021; Weintrop & Wilensky, 2015). Another limitation was that prominent, text-based integration activities, such as Bootstrap’s curricula in Algebra and Physics, are excluded. This selection criterion also notably excluded commonly used science simulation platforms, like NetLogo and PhET. These platforms include a large range of simulations for scientific phenomena and other models beyond science. While the simulations allow users to easily access the source code, the primary interface does not include the program used to create the simulation. In addition, the source code, except for some adapted NetLogo simulations, is text-based. Though the programs are heavily commented to make them understandable, they do not meet the inclusion criteria for the current dataset. More programming-centric and block-based options for scientific simulations, like StarLogo Nova, were included. Access Accessibility of the activities was the final criterion for inclusion. Following the accessibility criteria used by Lin and Weintrop (2021), we included activities only if they could be found online, were free of cost, did not require a physical device like robotics toolkits, and were updated recently enough that they ran on current versions of languages and operating systems. The requirement to be found online is not expected to substantially narrow the analysis because Lin and Weintrop found that 90% of block-based programming languages ran in a web browser. Exclusion for use of physical devices is a corollary to the requirement to be free of cost. We felt that these criteria would result in a dataset that had the broadest and most equitable applications because many public schools in low-income areas in the US cannot afford physical computing or robotics kits. Search Criteria Users need to recognize that the current dataset was based on a review of computing integration activities but not a systematic review. Unlike systematic literature reviews of scholarly work on a given keyword or topic area, there were no databases of indexed computing integration activities that span our inclusion criteria. Some repositories for certain languages exist, such as ScratchEd’s repository of Scratch projects and the Exploring Computational Thinking repository of Pencil Code and Python activities. However, computing integration activities are not published through a central organization, so they can be difficult to find. In lieu of a systematic review, we attempted to build a database that represented activities from a variety of disciplines, student ages, designers, and languages. To create this database, we included any activities that we were already aware of, such as Action Fractions, links from lists of computing integration activities, such as "Scratch Projects Across the Curriculum," links from CSforAll’s curriculum directory, and a general Google search for "‘integrated computing’ activities" and "‘computational thinking’ + programming" or "‘computational thinking’ + coding." We examined the first 100 returns for these searches. However, many of the activities found through Google search were excluded based on our criteria, primarily for not including non-CS learning objectives. We included activities as whole units, whether they were single-class lessons or extended curricular units that included multiple lessons, like Coding as Another Language. Treating individual lessons from curricular units as individual activities would have created an over-representation of extended units (e.g., 72 lessons for the Kindergarten, 1st, and 2nd grade curricular units from Coding as Another Language instead of 3 activities). Our database included 81 activities from the following sources: • CANON Lab • Code.org’s CS Connections • Code.org’s Hour of Code • Coding as Another Language curriculum • CS+ units from University of California San Diego • CSforALL’s Curriculum Repository (including 144 curricular units at the time of searching) • CT4Edu • Everyday Computing • Exploring Computational Thinking • Google search • Google’s CS First • Integrated computing activities from Georgia State University • Project GUTS • ScratchEd • The Tech Interactive • TVO Learn • UCL Scratch Maths We analyzed the distribution of these activities’ characteristics based on primary discipline, student age, programming language, and minimum time to complete. Based on discipline, we recognized that we had only two from history or social studies and searched for additional activities. While we found many projects on ScratchEd’s website, they did not meet the selection criteria. Required courses, including Language Arts, Math, and Science had a sufficient number of activities, matching their representation in the school day. We also had a wide range of activities based on student age and minimum time to complete, so we did not search for any additional activities based on these characteristics. To explore the representation in our database based on programming languages, we used the categories identified by Lin and Weintrop (2021) to ensure coverage of different types of block-based languages. The database has activities from Pencil Code (i.e., block-based implementation of a text-based language), Scratch (i.e., multimedia focused on animations and storytelling), AppLab (i.e., mobile app development), StarLogo Nova (i.e., simulations), and ScratchJr (i.e., pre-reading language). We decided against requiring languages from Lin and Weintrop’s other categories for data science, physical computing, and task-specific languages because they did not match our inclusion criteria. We explored other common languages to include, like Alice, Snap!, and App Inventor, but we did not find activities that matched our criteria.

Languages change over time, driven by creation of new words and cultural pressure to optimise communication. Programming languages resemble written language but communicate primarily with computer hardware rather than a human audience. I tested for changes over time in use of R, a mature, open-source programming language used for scientific computing. Across 393,142 GitHub repositories published between 2014 and 2021, I extracted 143,409,288 R functions, programming “verbs†, and paired linguistic and ecological approaches to estimate change in the diversity and composition of function use over time. I found that the number of R functions in use increased and underwent substantial change, driven primarily by the popularity of the “tidyverse†collection of community-written extensions. I provide evidence that users can directly change the nature of programming languages, with patterns that match known processes from natural languages and genetic evolution. In the case of R, patterns sugge..., Full methodology for this dataset is available in the manuscript, and the R code to process this is located in a GitHub repository: https://github.com/TimothyStaples/Revolution, This is a comma-separated values file intended for use in the galaxy_analysis.R script in https://github.com/TimothyStaples/Revolution

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

Online Java Learning Platform Market Outlook

The global market size of the online Java learning platform market was valued at approximately USD 1.2 billion in 2023 and is expected to reach USD 3.8 billion by 2032, growing at a robust CAGR of 13.5% during the forecast period. This growth is driven by increasing demand for Java programming skills across various industries, heightened by the boom in digital transformation initiatives and a growing focus on reskilling and upskilling the workforce.

One of the critical growth factors for this market is the rising adoption of digital learning platforms. The necessity for continuous learning in a rapidly changing technological landscape has driven individuals and organizations to seek online platforms for skill enhancement. Java, being one of the most popular programming languages, is highly sought after by both beginners and professionals, driving substantial growth in this market. Moreover, the flexibility and accessibility offered by online learning platforms have made it easier for people to learn at their own pace and convenience, further propelling market growth.

Another significant factor contributing to the market's growth is the increasing penetration of the internet and smartphones. With the proliferation of high-speed internet and affordable smartphones, online learning has become more accessible to a broader audience. This democratization of education has enabled learners from remote and underserved regions to access quality Java programming courses, thereby expanding the market's reach. Additionally, the ongoing advancements in mobile technology and the availability of various educational apps have made learning more interactive and engaging, attracting more users to online Java learning platforms.

The growing emphasis on lifelong learning and professional development is also a major driver for the market. In today's competitive job market, continuous skill development is essential for career growth and job security. Many enterprises are investing in upskilling their employees to stay ahead in the competitive landscape, leading to increased adoption of online Java learning platforms. These platforms offer a range of courses tailored to different skill levels and professional requirements, making them an attractive option for both individuals and organizations.

In addition to the convenience of online learning, the hybrid model of Online and Offline Coding Bootcamp is gaining traction among learners. This approach combines the flexibility of online courses with the hands-on experience of in-person sessions, offering a comprehensive learning experience. Students can benefit from the structured guidance of instructors during offline sessions while enjoying the flexibility of online resources at their own pace. This model is particularly appealing to those who appreciate the balance between self-directed learning and face-to-face interaction, making it an attractive option for a diverse range of learners.

From a regional perspective, North America holds the largest market share due to the presence of a significant number of tech-savvy individuals and a high demand for programming skills in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digitization of economies, government initiatives to promote digital education, and the increasing number of internet users. Europe and other regions are also anticipated to experience steady growth due to the rising awareness of the importance of programming skills and the benefits of online learning.

Course Type Analysis

The online Java learning platform market is segmented by course type into beginner, intermediate, and advanced levels. The beginner segment caters to individuals who are new to programming and wish to learn the basics of Java. This segment has seen significant growth due to the increasing number of students and professionals looking to enter the field of programming. Many online platforms offer introductory courses that cover fundamental concepts, syntax, and basic programming techniques, making it easier for beginners to get started with Java.

The intermediate segment targets learners who have a basic understanding of Java and want to enhance their skills further. This segment includes courses that delve deeper into Java programming concepts, such as object-oriented programming, data

The industry has experienced robust growth in recent years, largely driven by shifting consumer demands and advances in digital learning approaches. Major companies addressed the challenge of users’ shrinking attention spans by implementing gamified content and microlearning modules, allowing individuals to learn at their own pace and awarding points to boost motivation and retention rates. The rise of chatbot-integrated practice and extended virtual tutoring sessions marked another strategic response, particularly as critiques rose surrounding the long-term skill outcomes of app-based learning. Collaborative learning also became vital, with platforms offering video call features to improve direct engagement and enable dynamic interaction among users—a move that renewed interest and fostered measurable increases in platform engagement. As global demand grew, especially in markets where learners are focused on acquiring English proficiency, the development of AI-powered audio and listening exercises became a core industry focus. These enhancements introduced notable operational costs, regulating profit growth against significant avenues for user acquisition. Facilitative pricing models, particularly those featuring premium subscription tiers, insulated revenue and allowed expansion to continue in a competitive market. Over the five years leading up to 2025, these innovations and expansions yielded a revenue CAGR of 22.3%, enabling total industry revenue to reach a projected $6.7 billion in 2025 following consistent annual upticks, including 2.8% growth in that year alone. Prospects for the industry turn less bullish beyond 2025, as advancements in real-time verbal translation tools directly reduce the necessity for conventional language learning solutions. The diversification of online content away from an English-centric model challenges language apps’ primary revenue base. At the same time, textual translation software now offers much deeper contextual sophistication, offering language learners alternatives that were previously unavailable or unsophisticated. These downward pressures are likely to escalate, aggravated by the uptake of free American Sign Language (ASL) education technology that pushes down regional niche market revenue. Software providers are expected to increase investment in unique offerings, such as realistic conversational simulations and responsive tools to reinforce literacy acquisition, to withstand encroaching commoditization. Nonetheless, these necessary developments come with rising research and operational expenses, constraining industry profitability even as new opportunities emerge, particularly in communications-oriented business language training. Over the five years leading to 2030, revenue are forecast to contract at a 1.3% annual rate, reducing total market value to close to $6.3 billion as companies face heightened cost structures and intensified competitive headwinds.

Online Coding Bootcamps Market Outlook

The global market size for online coding bootcamps was valued at approximately $1.2 billion in 2023 and is projected to reach around $3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% over the forecast period. This significant growth can be attributed to the increasing demand for skilled software developers across various industries, driven by digital transformation initiatives and the rising importance of technological proficiency in the modern workforce.

One of the primary growth factors propelling the online coding bootcamps market is the surge in demand for web and software development skills. As technology continues to advance rapidly, businesses across industries are in dire need of professionals who can design, develop, and maintain complex software systems. This necessity has led to a growing trend of individuals enrolling in coding bootcamps to upskill and reskill themselves in high-demand programming languages and technologies. Furthermore, the flexibility and accessibility of online learning platforms have made these bootcamps a popular choice for both students and working professionals looking to enhance their skills without disrupting their daily routines.

Another significant factor contributing to market growth is the increasing affordability and effectiveness of online coding bootcamps compared to traditional higher education programs. Conventional computer science degrees often require significant time and financial investment, whereas coding bootcamps offer a more cost-effective and accelerated pathway to gaining technical skills. In addition, many bootcamps have established partnerships with tech companies, providing students with direct pathways to employment upon completion of their courses. This alignment with industry needs and the promise of tangible career outcomes make online coding bootcamps a compelling choice for many learners.

The widespread adoption of remote work practices, accelerated by the COVID-19 pandemic, has further fueled the demand for online coding bootcamps. As companies continue to embrace remote and hybrid work models, the need for skilled developers who can work independently from various locations has increased. Online coding bootcamps cater to this demand by offering flexible learning schedules and the opportunity to gain relevant skills from anywhere in the world. This shift has broadened the potential market for coding bootcamps, reaching a more diverse and global audience.

Programming Education has become a cornerstone of modern learning, especially as the demand for coding skills continues to soar across various sectors. The integration of programming into educational curricula is not just limited to higher education but is increasingly being introduced at earlier stages of schooling. This shift is driven by the recognition that programming skills are essential for future job markets, where digital literacy will be as fundamental as traditional literacy. As a result, educational institutions are partnering with coding bootcamps to provide students with practical, hands-on experience in programming, ensuring they are well-prepared for the technological challenges of tomorrow.

Regionally, North America currently dominates the online coding bootcamps market, driven by the high demand for tech talent and the presence of a mature technology sector. However, significant growth is anticipated in the Asia Pacific region, which is expected to witness the highest CAGR during the forecast period. This growth can be attributed to the rapid digitalization of economies, the increasing number of internet users, and the rising focus on enhancing technical education and skill development in countries like India and China.

Course Type Analysis

The online coding bootcamps market is segmented by course type, including Full-Stack Development, Front-End Development, Back-End Development, Data Science, Cybersecurity, and Others. Full-Stack Development courses are among the most popular, as they equip students with comprehensive skills in both front-end and back-end technologies. This versatility makes full-stack developers highly sought after in the job market, as they can handle multiple aspects of software development projects, making them valuable assets to any team.

Front-End Development courses focus on the client-side of web applications, teaching students how to design and develop user interfaces

The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RPC methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.

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.

This is the dataset containing the results from the user study questionnaire conducted at the TU Delft as part of the Master Thesis "Bringing Formal Verification into Widespread Programming Language Ecosystems" by Sára Juhošová. The objective of the research was to investigate and improve the usability of a Agda2HS, a tool for transpiling programs written in Agda into Haskell. The data within this dataset was collected through an online form as part of the user study designed to evaluate the usability of Agda2HS after new features were added. The questions were a mixture of Agree-Disagree statements and supplementary open questions, directed at the participants' experience and impressions of Agda2HS. The participants filled in this questionnaire after implementing two programming assignments using Agda2HS.

General authorship identification is essential to the detection of undesirable deception of others' content misuse or exposing the owners of some anonymous hurtful content. This is done by revealing the author of that content. Authorship Identification of SOurce COde (AI-SOCO) focuses on uncovering the author who wrote some piece of code. This facilitates solving issues related to cheating in academic, work and open source environments. Also, it can be helpful in detecting the authors of malware softwares over the world.

The detection of cheating in academic communities is significant to properly address the contribution of each researcher. Also, in work environments, credit sometimes goes to people that did not deserve it. Such issues of plagiarism could arise in open source projects that are available on public platforms. Similarly, this could be used in public or private online coding contests whether done in coding interviews or in official coding training contests to detect the cheating of applicants or contestants. A system like this could also play a big role in detecting the source of anonymous malicious softwares.

The dataset is composed of source codes collected from the open submissions in the Codeforces online judge. Codeforces is an online judge for hosting competitive programming contests such that each contest consists of multiple problems to be solved by the participants. A Codeforces participant can solve a problem by writing a solution for it using any of the available programming languages on the website, and then submitting the solution through the website. The solution's result can be correct (accepted) or incorrect (wrong answer, time limit exceeded, etc.).

In our dataset, we selected 1,000 users and collected 100 source codes from each one. So, the total number of source codes is 100,000. All collected source codes are correct, bug-free, compile-ready and written using the C++ programming language using different versions. For each user, all collected source codes are from unique problems.

Given the pre-defined set of source codes and their authors, the task is to build a system to determine which one of these authors wrote a given unseen before source code.

Dataset website: https://sites.google.com/view/ai-soco-2020.

JavaScript Development Services Market Outlook

The global JavaScript Development Services market size was valued at USD 8.5 billion in 2023 and is projected to reach USD 19.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.3% during the forecast period. The growth of this market is driven by the increasing demand for dynamic and interactive web applications, the proliferation of online businesses, and the rising adoption of cloud-based services.

One of the primary growth factors for the JavaScript Development Services market is the exponential rise in internet usage and the subsequent demand for web-based solutions. Businesses across various sectors are increasingly seeking robust and scalable web applications to improve customer engagement and streamline operations. JavaScript, being a versatile and widely-used programming language, plays a crucial role in developing such applications. Moreover, the continuous advancements in JavaScript frameworks and libraries, such as React, Angular, and Vue.js, have enhanced the efficiency and capabilities of web development, further propelling market growth.

Another significant growth driver is the shift towards mobile-first strategies and the increasing demand for mobile applications. Organizations are investing heavily in mobile app development to cater to the growing number of smartphone users worldwide. JavaScript, with its powerful frameworks like React Native, has become a popular choice for mobile app development, as it allows developers to build cross-platform applications efficiently. This trend is expected to continue, contributing significantly to the expansion of the JavaScript Development Services market.

The rise of cloud computing has also positively impacted the JavaScript Development Services market. The adoption of cloud-based solutions offers numerous benefits, including scalability, flexibility, and cost-effectiveness. Many businesses prefer deploying their applications on cloud platforms, which necessitates the need for cloud-compatible JavaScript development services. Additionally, the integration of JavaScript with various cloud services and APIs has simplified the development process, making it more accessible and efficient for developers.

Regionally, North America holds a significant share of the JavaScript Development Services market due to the presence of numerous tech giants and a high adoption rate of advanced technologies. The Asia Pacific region is expected to witness the highest growth rate, driven by the rapid digital transformation, increasing internet penetration, and the booming e-commerce industry. Europe also presents substantial growth opportunities, supported by the strong demand for innovative web and mobile applications across various industries.

Service Type Analysis

The JavaScript Development Services market is segmented by service type into Custom Development, Web Development, Mobile App Development, Enterprise Solutions, and Maintenance and Support. Each of these segments caters to different aspects of development needs and offers unique advantages to businesses.

Custom Development services encompass tailored solutions that meet specific business requirements. These services are highly sought after by enterprises looking for bespoke applications that align perfectly with their operational workflows. Custom development allows for a high degree of customization and flexibility, ensuring that the application can adapt to changing business needs. This segment is expected to grow steadily as more businesses prioritize personalized solutions to gain a competitive edge.

Web Development is a core segment within the JavaScript Development Services market. It involves creating and maintaining websites and web applications using JavaScript frameworks and libraries. The demand for web development services is driven by the increasing need for dynamic, user-friendly, and responsive websites. This segment benefits from continuous innovations in JavaScript technologies, which enhance development speed and application performance. As businesses increasingly rely on their online presence, the web development segment is poised for substantial growth.

Mobile App Development services are crucial in today's mobile-centric world. JavaScript frameworks like React Native and Ionic enable developers to build high-quality cross-platform mobile applications efficiently. The demand for mobile app development services has surged due to the proliferation of smartphones and the growing preference for mo