Tag Archives: statistics

Google Summer of Code 2020 Statistics: Part 2

With the program nearing the end of the summer, it’s time for another round of updates!

Universities

The 1,198 students accepted into the GSoC 2020 program came from 550 universities, of which, 114 have students participating for the first time in GSoC.

Schools with the most accepted students for GSoC 2020:
University# of Accepted Students
Indian Institute of Technology, Roorkee48
Indian Institute of Technology, Kanpur27
International Institute of Information Technology, Hyderabad24
National Institute of Technology Karnataka, Surathkal23
Birla Institute of Technology and Science, Pilani (BITS Pilani)13
Indian Institute of Technology, Kharagpur13
Indian Institute of Technology (BHU), Varanasi11
University of Moratuwa11
National Institute of Technology, Hamirpur10
Amrita Vishwa Vidyapeetham, Amritapuri Campus10
University of Tokyo10
University Of Colombo School Of Computing (UCSC)10

Mentors

Each year we pore over gobs of data to extract some interesting statistics about the GSoC mentors. Here’s a quick synopsis of our 2020 crew:
  • Registered mentors: 3,592
  • Mentors with assigned student projects: 2,156
  • Mentors who have participated in GSoC for 10 or more years: 78
  • Mentors who have been a part of GSoC for 5 years or more: 199
  • Mentors that are former GSoC students: 533 (24.7%)
  • Mentors that have also been involved in the Google Code-in program: 405 (18.8%)
  • Percentage of new mentors: 34.18%
GSoC 2020 had an international representation with mentors from 67 countries around the world!

The global pandemic, COVID-19, brought additional challenges to this year’s GSoC program. Whether living with the virus, adjusting to shifting school and work schedules, or pivoting to a remote lifestyle, students and mentors have had to prioritize their safety and delicately balance their new way of life. Despite these unprecedented times, our students continue to push on and our mentors fully support our students by sharing their passion for open source, listening to their concerns and providing them with valuable advice. For that commitment, we would like to acknowledge and give thanks to all students and mentors in the GSoC 2020 program. Not even a pandemic can dampen your enthusiasm and tireless contributions to the open source community!

By Stephanie Taylor – Program Manager, Google Open Source Programs Office

Open source by the numbers at Google

At Google, open source is at the core of our infrastructure, processes, and culture. As such, participation in these communities is vital to our productivity. Within OSPO (Open Source Programs Office), our mission is to bring the value of open source to Google and the resources of Google to open source. To ensure our actions match our commitment, in this post we will explore a variety of metrics intended to increase context, transparency, and accountability across all of the communities we engage with.

Why we contribute: Open source has become a pervasive component in modern software development, and Google is no exception. We use thousands of open source projects across our internal infrastructure and products. As participants in the ecosystem, our intentions are twofold: give back to the communities we depend on as well as expand support for open source overall. We firmly believe in open source and its ability to bring together users, contributors, and companies alike to deliver better software.

The majority of Google’s open source work is done within one of two hosting platforms: GitHub and git-on-borg, Google’s production Git service which integrates with Gerrit for code review and access control. While we also allow individual usage of Bitbucket, GitLab, Launchpad, and other platforms, this analysis will focus on GitHub and git-on-borg. We will continue to explore how best to incorporate activity across additional channels.

A little context about the numbers you’ll read below:
  • Business and personal: While git-on-borg hosts both internal and external Google created repos, GitHub is a mixture of Google projects, experimental efforts and personal projects created by Googlers.
  • Driven by humans: We have created many automated bots and systems that can propose changes on both hosting platforms. We have intentionally filtered these data to ensure we are only showing human initiated activities.
  • GitHub data: We are using GH Archive as the primary source for GitHub data, which is currently available as a public dataset on BigQuery. Google activity within GitHub is identified by self registered accounts, which we anticipate under reports actual usage as employees acclimate to our policies.
  • Active counts: Where possible, we will show ‘active users’ and ‘active repositories’ defined by logged activity within each specified timeframe (for GH archive data, that’s any event type logged in the public GitHub event stream).
As numbers mean nothing without scale, let’s start by defining our applicable community: In 2019, more than 9% of Alphabet’s full time employees actively contributed to public repositories on git-on-borg and GitHub. While single digit, this percentage represents a portion of all full time Alphabet employees—from engineers to marketers to admins, across every business unit in Alphabet—and does not include those who contribute to open source projects outside of code. As our population has grown, so has our registered contributor base:
This chart shows the aggregate per year counts of Googlers active on public repositories hosted on GitHub and git-on-borg

What we create: As mentioned above, our contributing population works across a variety of Google, personal, and external repositories. Over the years, Google has released thousands of open source projects (many of which span multiple repositories) and ~2,600 are still active. Today, Google hosts over 8,000 public repositories on GitHub and more than 1,000 public repositories on git-on-borg. Over the last five years, we have doubled the number of public repos, growing our footprint by an average of 25% per year.

What we work on: In addition to our own repositories, we contribute to a wide pool of external projects. In 2019, Googlers were active in over 70,000 repositories on GitHub, pushing commits and/or opening pull requests on over 40,000 repositories. Note that more than 75% of the repos with Googler-opened pull requests were outside of Google-managed organizations (on GitHub).
This charts shows per year counts of activities initiated by Googlers on GitHub

What we contribute: For contribution volume on GitHub, we chose to focus on push events, opened, and merged pull requests instead of commits as this metric on its own is difficult to contextualize. Note that push events and pull requests typically include one or more commits per event. In 2019, Googlers created over 570,000 issues, opened over 150,000 pull requests, and created more than 36,000 push events on GitHub. Since 2015, we have doubled our annual counts of issues created and push events, and more than tripled the number of opened pull requests. Over the last five years, more than 80% of pull requests opened by Googlers have been closed and merged into active repositories.

How we spend our time: Combining these two classes of metrics—contributions and repos—provides context on how our contributors focus their time. On GitHub: in 2015, about 40% of our opened pull requests were concentrated in just 25 repositories. However, over the next four years, our activity became more distributed across a larger set of projects, with the top 25 repos claiming about 20% of opened pull requests in 2019. For us, this indicates a healthy expansion and diversification of interests, especially given that this activity represents both Google, as well as a community of contributors that happen to work at Google.
This chart splits the total per year counts of Googler created pull requests on GitHub by Top 25 repos vs the remainder ranked by number of opened pull requests per repo per year.

Open source contribution is about more than code

Every day, Google relies on the health and continuing availability of open source, and as such we actively invest in the security and sustainability of open source and its supply chain in three key areas:
  • Security: In addition to building security projects like OpenTitan and gVisor, Google’s OSS-Fuzz project aims to help other projects identify programming errors in software. As of the end of 2019, OSS-Fuzz had over 250 projects using the project, filed over 16,000 bugs, including 3,500 security vulnerabilities.
  • Community: Open source projects depend on communities of diverse individuals. We are committed to improving community sustainability and growth with programs like Google Summer of Code and Season of Docs. Over the last 15 years, about 15,000 students from over 105 countries have participated in Google Summer of Code, along with 25,000 mentors in more than 115 countries working on more than 680 open source projects.
  • Research: At the end of 2019, Google invested $1 million in open source research, partnering with researchers at UVM, with the goal to deepen understanding of how people, teams and organizations thrive in technology-rich settings, especially in open-source projects and communities.
Learn more about our open source initiatives at opensource.google.

By Sophia Vargas – Researcher, Google Open Source Programs Office

Exploring Faster Screening with Fewer Tests via Bayesian Group Testing



How does one find a needle in a haystack? At the turn of World War II, that question took on a very concrete form when doctors wondered how to efficiently detect diseases among those who had been drafted into the war effort. Inspired by this challenge, Robert Dorfman, a young statistician at that time (later to become Harvard professor of economics), proposed in a seminal paper a 2-stage approach to detect infected individuals, whereby individual blood samples first are pooled in groups of four before being tested for the presence or absence of a pathogen. If a group is negative, then it is safe to assume that everyone in the group is free of the pathogen. In that case, the reduction in the number of required tests is substantial: an entire group of four people has been cleared with a single test. On the other hand, if a group tests positive, which is expected to happen rarely if the pathogen’s prevalence is small, at least one or more people within that group must be positive; therefore, a few more tests to determine the infected individuals are needed.
Left: Sixteen individual tests are required to screen 16 people — only one person’s test is positive, while 15 return negative. Right: Following Dorfman’s procedure, samples are pooled into four groups of four individuals, and tests are executed on the pooled samples. Because only the second group tests positive, 12 individuals are cleared and only those four belonging to the positive group need to be retested. This approach requires only eight tests, instead of the 16 needed for an exhaustive testing campaign.
Dorfman’s proposal triggered many follow-up works with connections to several areas in computer science, such as information theory, combinatorics or compressive sensing, and several variants of his approach have been proposed, notably those leveraging binary splitting or side knowledge on individual infection probability rates. The field has grown to the extent that several sub-problems are recognized and deserving of an entire literature on their own. Some algorithms are tailored for the noiseless case in which tests are perfectly reliable, whereas some consider instead the more realistic case where tests are noisy and may produce false negatives or positives. Finally, some strategies are adaptive, proposing groups based on test results already observed (including Dorfman’s, since it proposes to re-test individuals that appeared in positive groups), whereas others stick to a non-adaptive setting in which groups are known beforehand or drawn at random.

In “Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design”, we present an approach to group testing that can operate in a noisy setting (i.e., where tests can be mistaken) to decide adaptively by looking at past results which groups to test next, with the goal to converge on a reliable detection as quickly, and with as few tests, as possible. Large scale simulations suggest that this approach may result in significant improvements over both adaptive and non-adaptive baselines, and are far more efficient than individual tests when disease prevalence is low. As such, this approach is particularly well suited for situations that require large numbers of tests to be conducted with limited resources, as may be the case for pandemics, such as that corresponding to the spread of COVID-19. We have open-sourced the code to the community through our GitHub repo.

Noisy and Adaptive Group Testing in a Non-Asymptotic Regime
A group testing strategy is an algorithm that is tasked with guessing who, among a list of n people, carries a particular pathogen. To do so, the strategy provides instructions for pooling individuals into groups. Assuming a laboratory can execute k tests at a time, the strategy will form a kn pooling matrix that defines these groups. Once the tests are carried out, the results are used to decide whether sufficient information has been gathered to determine who is or is not infected, and if not, how to form new groups for another round of testing.

We designed a group testing approach for the realistic setting where the testing strategy can be adaptive and where tests are noisy — the probability that the test of an infected sample is positive (sensitivity) is less than 100%, as is the specificity, the probability that a non-infected sample returns negative.

Screening More People with Fewer Tests Using Bayesian Optimal Experimental Design
The strategy we propose proceeds the way a detective would investigate a case. They first form several hypotheses about who may or may not be infected, using evidence from all tests (if any) that have been carried out so far and prior information on the infection rate (a). Using these hypotheses, our detectives produce an actionable item to continue the investigation, namely a next wave of groups that may help in validating or invalidating as many hypotheses as possible (b), and then loop back to (a) until the set of plausible hypotheses is small enough to unambiguously identify the target of the search. More precisely,
  1. Given a population of n people, an infection state is a binary vector of length n that describes who is infected (marked with a 1), and who is not (marked with a 0). At a certain time, a population is in a given state (most likely a few 1’s and mostly 0’s). The goal of group testing is to identify that state using as few tests as possible. Given a prior belief on the infection rate (the disease is rare) and test results observed so far (if any), we expect that only a small share of those infection states will be plausible. Rather than evaluating the plausibility of all 2n possible states (an extremely large number even for small n), we resort to a more efficient method to sample plausible hypotheses using a sequential Monte Carlo (SMC) sampler. Although quite costly by common standards (a few minutes using a GPU in our experimental setup), we show in this work that SMC samplers remain tractable even for large n, opening new possibilities for group testing. In short, in return for a few minutes of computations, our detectives get an extensive list of thousands of relevant hypotheses that may explain tests observed so far.

  2. Equipped with a relevant list of hypotheses, our strategy proceeds, as detectives would, by selectively gathering additional evidence. If k tests can be carried out at the next iteration, our strategy will propose to test k new groups, which are computed using the framework of Bayesian optimal experimental design. Intuitively, if k=1 and one can only propose a single new group to test, there would be clear advantage in building that group such that its test outcome is as uncertain as possible, i.e., with a probability that it returns positive as close to 50% as possible, given the current set of hypotheses. Indeed, to progress in an investigation, it is best to maximize the surprise factor (or information gain) provided by new test results, as opposed to using them to confirm further what we already hold to be very likely. To generalize that idea to a set of k>1 new groups, we score this surprise factor by computing the mutual information of these “virtual” group tests vs. the distribution of hypotheses. We also consider a more involved approach that computes the expected area under the ROC curve (AUC) one would obtain from testing these new groups using the distribution of hypotheses. The maximization of these two criteria is carried out using a greedy approach, resulting in two group selectors, GMIMAX and GAUCMAX (greedy maximization of mutual information or AUC, respectively).
The interaction between a laboratory (wet_lab) carrying out testing, and our strategy, composed of a sampler and a group selector, is summarized in the following drawing, which uses names of classes implemented in our open source package.
Our group testing framework describes an interaction between a testing environment, the wet_lab, whose pooled test results are used by the sampler to draw thousands of plausible hypotheses on the infection status of all individuals. These hypotheses are then used by an optimization procedure, group_selector, that figures out what groups may be the most relevant to test in order to narrow down on the true infection status. Once formed, these new groups are then tested again, closing the loop. At any point in the procedure, the hypotheses formed by the sampler can be averaged to obtain the average probability of infection for each patient. From these probabilities, a decision on whether a patient is infected or not can be done by thresholding these probabilities at a certain confidence level.
Benchmarking
We benchmarked our two strategies GMIMAX and GAUCMAX against various baselines in a wide variety of settings (infection rates, test noise levels), reporting performance as the number of tests increases. In addition to simple Dorfman strategies, the baselines we considered included a mix of non-adaptive strategies (origami assays, random designs) complemented at later stages with the so-called informative Dorfman approach. Our approaches significantly outperform the others in all settings.
We executed 5000 simulations on a sample population of 70 individuals with an infection rate of 2%. We have assumed sensitivity/specificity values of 85% / 97% for tests with groups of maximal size 10, which are representative of current PCR machines. This figure demonstrates that our approach outperforms the other baselines with as few as 24 tests (up to 8 tests used in 3 cycles), including both adaptive and non-adaptive varieties, and performs significantly better than individual tests (plotted in the sensitivity/specificity plane as a hexagon, requiring 70 tests), highlighting the savings potential offered by group testing. See preprint for other setups.
Conclusion
Screening a population for a pathogen is a fundamental problem, one that we currently face during the current COVID-19 epidemic. Seventy years ago, Dorfman proposed a simple approach currently adopted by various institutions. Here, we have proposed a method to extend the basic group testing approach in several ways. Our first contribution is to adopt a probabilistic perspective, and form thousands of plausible hypotheses of infection distributions given test outcomes, rather than trust test results to be 100% reliable as Dorfman did. This perspective allows us to seamlessly incorporate additional prior knowledge on infection, such as when we suspect some individuals to be more likely than others to carry the pathogen, based for instance on contact tracing data or answers to a questionnaire. This provides our algorithms, which can be compared to detectives investigating a case, the advantage of knowing what are the most likely infection hypotheses that agree with prior beliefs and tests carried out so far. Our second contribution is to propose algorithms that can take advantage of these hypotheses to form new groups, and therefore direct the gathering of new evidence, to narrow down as quickly as possible to the "true" infection hypothesis, and close the case with as little testing effort as possible.

Acknowledgements
We would like to thank our collaborators on this work, Olivier Teboul, in particular, for his help preparing figures, as well as Arnaud Doucet and Quentin Berthet. We also thank Kevin Murphy and Olivier Bousquet (Google) for their suggestions at the earliest stages of this project, as well as Dan Popovici for his unwavering support pushing this forward; Ignacio Anegon, Jeremie Poschmann and Laurent Tesson (INSERM) for providing us background information on RT-PCR tests and Nicolas Chopin (CREST) for giving guidance on his work to define SMCs for binary spaces.

Source: Google AI Blog


Google Summer of Code 2019 (Statistics Part 2)

2019 has been an epic year for Google Summer of Code as we celebrated 15 years of connecting university students from around the globe with 201 open source organizations big and small.

We want to congratulate our 1,134 students that complete GSoC 2019. Great work everyone!

Now that GSoC 2019 is over we would like to wrap up the program with some more statistics to round out the year.

Student Registrations

We had 30,922 students from 148 countries register for GSoC 2019 (that’s a 19.5% increase in registrations over last year, the previous record). Interest in GSoC clearly continues to grow and we’re excited to see it growing in all parts of the world.

For the first time ever we had students register from Bhutan, Fiji, Grenada, Papua New Guinea, South Sudan, and Swaziland.

Universities

The 1,276 students accepted into the GSoC 2019 program hailed from 6586 universities, of which, 164 have students participating for the first time in GSoC.

Schools with the most accepted students for GSoC 2019:

University # of Accepted Students
Indian Institute of Technology, Roorkee48
International Institute of Information Technology - Hyderabad29
Birla Institute of Technology and Science, Pilani (BITS Pilani)27
Guru Gobind Singh Indraprastha University (GGSIPU Dwarka)20
Indian Institute of Technology, Kanpur19
Indian Institute of Technology, Kharagpur19
Amrita University / Amrita Vishwa Vidyapeetham14
Delhi Technological University11
Indian Institute of Technology, Bombay11
Indraprastha Institute of Information and Technology, New Delhi11

Mentors

Each year we pore over gobs of data to extract some interesting statistics about the GSoC mentors. Here’s a quick synopsis of our 2019 crew:
  • Registered mentors: 2,815
  • Mentors with assigned student projects: 2,066
  • Mentors who have participated in GSoC for 10 or more years: 70
  • Mentors who have been a part of GSoC for 5 years or more: 307
  • Mentors that are former GSoC students: 691
  • Mentors that have also been involved in the Google Code-in program: 498
  • Percentage of new mentors: 35.84%
GSoC 2019 mentors are from all parts of the world, representing 81 countries!

Every year thousands of GSoC mentors help introduce the next generation to the world of open source software development—for that we are forever grateful. We can not stress enough that without our invaluable mentors the GSoC program would not exist. Mentorship is why GSoC has remained strong for 15 years, the relationships built between students and mentors have helped sustain the program and many of these communities. Sharing their passion for open source, our mentors have paved the road for generations of contributors to enter open source development.

Thank you to all of our mentors, organization administrators, and all of the “unofficial” mentors that help in our open source organization’s communities. Google Summer of Code is a community effort and we appreciate each and every one of you.

By Stephanie Taylor, Google Open Source

Reflecting on Google Code-in 2018

Google Code-in (GCI), our contest introducing 13-17 year olds to open source software development, wrapped up last December with impressive numbers: 3,124 students from 77 countries completed an impressive 15,323 tasks!

These students spent 7 weeks working online with 27 open source organizations, writing code, writing and editing documentation, designing UI elements and logos, conducting research, developing videos teaching others about open source software, as well as finding (and fixing!) hundreds of bugs.

Overview

  • 2,164 students completed three or more tasks (earning a Google Code-in 2018 t-shirt)
  • 17% of students were girls
  • 23% of the participants from the USA were girls
  • 79% of students were first time participants in GCI
  • We saw very large increases in the number of students from Austria, Indonesia, Malaysia, Pakistan, and Taiwan

Student Age

Participating Schools

Students from 1,673 schools competed in this year’s contest. Many students learn about GCI from their friends or teachers and continue to spread the word to their classmates. This year the 5 schools with the most students completing tasks in the contest were:
School Name Number of Student Participants Country
Dunman High School 110 Singapore
Indus E.M High School 73 India
Sacred Heart Convent Senior Secondary School 69 India
Amity International School Sec-46 Gurgaon 36 India
Bhartiya Vidya Bhavan Vidyashram Pratap Nagar 27 India

Countries

We are pleased to have 9 countries with first time Winners and Finalists. Winners from Georgia, Macedonia, Philippines, South Africa and Spain, and Finalists from Israel, Luxembourg, Nepal and Pakistan.

The chart below displays the 10 countries with the most students completing at least 1 task.

What's Next

In June we will welcome all 54 grand prize winners to the San Francisco Bay Area for a fun-filled trip. The trip includes the opportunity for students to meet with one of the mentors they worked with during the contest. Students will also take part in an awards ceremony, meet with Google engineers to hear about new and exciting projects, tours of the Google campuses and a fun day exploring San Francisco.

We are thrilled that Google Code-in was so popular this year. We hope to continue to grow and expand this contest in the future to introduce even more teenagers to the world of open source software development.

Thank you again to the heroes of this program: the 789 mentors from 57 countries that guided students through the program and welcomed them into their open source communities.

By Saranya Sampat, Google Open Source

Magnificent mentors of Google Summer of Code 2018

Mentors are the heart and soul of the Google Summer of Code (GSoC) program and have been for the last 14 years. Without their hard work and dedication, there would be no Google Summer of Code. These volunteers spend 4+ months guiding their students to create the best quality project possible while welcoming them into their communities – answering questions and providing help at all hours of the day, including weekends and holidays.

Thank you mentors and organization administrators! 

Each year we pore over heaps of data to extract some interesting statistics about the GSoC mentors. Here’s a quick synopsis of our 2018 crew:
  • Registered mentors: 2,819
  • Mentors with assigned student projects: 1,996
  • Mentors who have participated in GSoC for 10 or more years: 46
  • Mentors who have been a part of GSoC for 5 years or more: 272
  • Mentors that are former GSoC students: 627
  • Mentors that have also been involved in the Google Code-in program: 474
  • Percentage of new mentors: 36.5%
GSoC 2018 mentors are from all parts of the world, hailing from 75 countries!

If you want to see the stats for all 75 countries check out this list.


Another fun fact about our 2018 mentors: they range in age from 15-80 years old!
  • Average mentor age: 34
  • Median mentor age: 33
  • Mentors under 18 years old: 26*
GSoC mentors help introduce the next generation to the world of open source software development – for that we are very grateful. To show our appreciation, we invite two mentors from each of the 206 participating organizations to attend our annual mentor summit at the Google campus in Sunnyvale, California. It’s three days of community building, lively debate, learning best practices from one another, working to strengthen open source communities, good food, and lots and lots of chocolate.

Thank you to all of our mentors, organization administrators, and all of the “unofficial” mentors that help in the various open source organization’s communities. Google Summer of Code is a community effort and we appreciate each and every one of you.

Cheers to yet another great year!

By Stephanie Taylor, Google Open Source

* Most of these 26 young GSoC mentors started their journey in Google Code-in, our contest for 13-17 year olds that introduces young students to open source software development.

Google Summer of Code 2018 statistics part 2

Now that Google Summer of Code (GSoC) 2018 is underway and students are wrapping up their first month of coding, we wanted to bring you some more statistics on the 2018 program. Lots and lots of numbers follow:

Organizations

Students are working with 206 organizations (the most we’ve ever had!), 41 of which are participating in GSoC for the first time.

Student Registrations

25,873 students from 147 countries registered for the program, which is a 25.3% increase over the previous high for the program back in 2017. There are 9 new countries with students registering for the first time: Angola, Bahamas, Burundi, Cape Verde, Chad, Equatorial Guinea, Kosovo, Maldives, and Mali.

Project Proposals

5,199 students from 101 countries submitted a total of 7,209 project proposals. 70.5% of the students submitted 1 proposal, 18.1% submitted 2 proposals, and 11.4% submitted 3 proposals (the max allowed).

Gender Breakdown

11.63% of accepted students are women, a 0.25% increase from last year. We are always working toward making our programs and open source more inclusive, and we collaborate with organizations and communities that help us improve every year.

Universities

The 1,268 students accepted into the GSoC 2018 program hailed from 613 universities, of which 216 have students participating for the first time in GSoC.

Schools with the most accepted students for GSoC 2018:
University Country Students
Indian Institute of Technology, Roorkee India 35
International Institute of Information Technology - Hyderabad India 32
Birla Institute of Technology and Science, Pilani (BITS Pilani) India 23
Indian Institute of Technology, Kharagpur India 22
Birla Institute of Technology and Science Pilani, Goa campus / BITS-Pilani - K.K.Birla Goa Campus India 18
Indian Institute of Technology, Kanpur India 16
University of Moratuwa Sri Lanka 16
Indian Institute of Technology, Patna India 14
Amrita University India 13
Indian Institute of Technology, Mandi India 11
Indraprastha Institute of Information and Technology, New Dehli India 11
University of Buea Cameroon 11
BITS Pilani, Hyderabad Campus India 11
Another post with stats on our awesome GSoC mentors will be coming soon!

By Stephanie Taylor, Google Open Source

Google Summer of Code 2018 statistics part 1

Since 2005, Google Summer of Code (GSoC) has been bringing new developers into the open source community every year. This year we accepted 1,264 students from 62 countries into the 2018 GSoC program to work with a record 206 open source organizations this summer.

Students are currently participating in the Community Bonding phase of the program where they become familiar with the open source projects they will be working with. They also spend time learning the codebase and the community’s best practices so they can start their 12 week coding projects on May 14th.

Each year we like to share program statistics about the GSoC program and the accepted students and mentors involved in the program. Here are a few stats:
  • 88.2% of the accepted students are participating in their first GSoC
  • 74.4% of the students are first time applicants

Degrees

  • 76.18% of accepted students are undergraduates, 17.5% are masters students, and 6.3% are getting their PhDs.
  • 73% are Computer Science majors, 4.2% are mathematics majors, 17% are other engineering majors (electrical, mechanical, aerospace, etc.)
  • We have students in a variety of majors including neuroscience, linguistics, typography, and music technologies.

Countries

This year there are four students that are the first to be accepted into GSoC from their home countries of Kosovo (three students) and Senegal. A complete list of accepted students and their countries is below:
CountryStudentsCountryStudentsCountryStudents
Argentina5Hungary7Russian Federation35
Australia10India605Senegal1
Austria14Indonesia3Serbia1
Bangladesh3Ireland1Singapore8
Belarus3Israel2Slovak Republic2
Belgium3Italy24South Africa1
Brazil19Japan7South Korea2
Bulgaria2Kosovo3Spain21
Cameroon14Latvia1Sri Lanka41
Canada31Lithuania5Sweden6
China52Malaysia2Switzerland5
Croatia3Mauritius1Taiwan3
Czech Republic4Mexico4Trinidad and Tobago1
Denmark1Morocco2Turkey8
Ecuador4Nepal1Uganda1
Egypt12Netherlands6Ukraine6
Finland3Nigeria6United Kingdom28
France22Pakistan5United States104
Germany53Poland3Venezuela1
Greece16Portugal10Vietnam4
Hong Kong3Romania10Venezuela1
There were a record number of students submitting proposals for the program this year -- 5,199 students from 101 countries.

In our next GSoC statistics post we will delve deeper into the schools, gender breakdown, mentors, and registration numbers for the 2018 program.

By Stephanie Taylor, Google Open Source

Google Code-in 2017: more is merrier!

Google Code-in Logo
Google Code-in (GCI), our contest introducing 13-17 year olds to open source software development, wrapped up last month with jaw-dropping numbers: 3,555 students from 78 countries completed an impressive 16,468 tasks! That’s 265% more students than last year - the previous high during the 7 year contest!

These students spent 7 weeks working online with 25 open source organizations, writing code, writing and editing documentation, designing UI elements and logos, conducting research, developing videos teaching others about open source software, as well as finding (and fixing!) hundreds of bugs.

General Statistics

  • 65.9% of students completed three or more tasks (earning a Google Code-in 2017 t-shirt)
  • 17% of students were girls
  • 27% of the participants from the USA were girls
  • 91% of the students were first time participants

Student Age

Participating Schools

Students from 2,060 schools competed in this year’s contest. Many students learn about GCI from their friends or teachers and continue to spread the word to their classmates. This year the 5 schools with the most students completing tasks in the contest were:

School Name Number of Student Participants Country
Dunman High School 140 Singapore
Sacred Heart Convent Senior Secondary School 43 India
Indus E.M High School 27 India
Jayshree Periwal International School 25 India
Union County Magnet High School 18 United States

Countries

We are pleased to have 7 new countries participating in GCI this year: Bolivia, Botswana, Guinea, Guyana, Iceland, Kyrgyzstan, and Morocco! The chart below displays the ten countries with the most students completing at least 1 task.


In June we will welcome all 50 grand prize winners to the San Francisco Bay Area for a fun-filled trip. The trip includes the opportunity for students to meet with one of the mentors they worked with during the contest. Students will also take part in an awards ceremony, meet with Google engineers to hear about new and exciting projects, tours of the Google campuses and a fun day exploring San Francisco. 

Keep an eye on the Google Open Source Blog in the coming weeks for posts from mentoring organizations describing their experience and the work done by students.

We are thrilled that Google Code-in was so popular this year. We hope to continue to grow and expand this contest in the future to introduce even more teenagers to the world of open source software development. 

Thank you again to the heroes of this program: the 704 mentors from 62 countries that guided students through the program and welcomed them into their open source communities.

By Stephanie Taylor, Google Code-in Team

Google Code-in is breaking records

It’s been an incredible (and incredibly busy!) three weeks for the 25 mentor organizations participating in Google Code-in (GCI) 2017, our seven week global contest designed to introduce teens to open source software development. Participants complete bite sized “tasks” in topics that include coding, documentation, UI/UX, quality assurance and more. Volunteer mentors from each open source project help participants along the way.

Total registered students has already surpassed 2016 numbers and we are less than halfway to the finish! We’re thrilled that high school students are embracing GCI like never before.

Check out some of the statistics below (current as of Thursday, December 14):
  • Total registered students: 6,146
  • Number of students who have completed at least one task: 1,573 (51% of those students have completed more than 3 tasks, earning them a GCI t-shirt)
  • Total number of tasks completed: 5,499
  • Most tasks completed by one student: 39

Top 5 Countries by Tasks Completed

Countries Represented by Mentors and Students



Of course, GCI wouldn’t be possible without the effort of the more than 725 mentors and organization administrators. Based in 65 countries, mentors answer questions, review submissions, and approve tasks for students at all hours of the day -- and sometimes night! They work tirelessly to help encourage and guide the next generation of open source contributors.

Every year we express our gratitude to the mentors and organization administrators. We are particularly grateful for them given how many more students are participating in GCI this year. Thank you all, and hang in there!

By Mary Radomile, Google Open Source