Methodology and Demographics
Developer Ecosystem Survey 2026
The Developer Ecosystem Report 2026 is based on a survey of 28,945 developers from 198 countries conducted between May and July 2026.
The survey methodology supports data quality and representativeness through several steps:
- Rigorous data cleaning to eliminate fraudulent responses.
- Randomization and complex survey logic across 535 questions.
- Targeting and localization strategies to support unbranded and representative recruitment.
- A three-stage weighting process to balance the data.
The State of the Developer Ecosystem 2026 is a public report. Its contents may be used only for non-commercial purposes, as described here.
Demographics
Age distribution
Full years of professional coding experience
Gender
Male
Female
Non-binary, genderqueer, or gender non-conforming
I’d prefer not to say
Respondents by countries and regions
Methodology
This data cleaning process ensures that only high-quality responses are included in the report, excluding fraudulent and dishonest answers. We used 91 criteria to identify and exclude suspicious responses, which is almost double the criteria we used last year. They included:
- Surveys that were filled out too fast.
- Surveys with conflicting answers, for example, “18–20 years old” combined with “more than 16 years of professional experience”.
- Surveys with only a single option chosen for almost all the multiple-selection questions.
- Multiple surveys submitted from the same email address. In such cases, we kept the response that was the most complete.
Reducing the response burden
This year's survey consisted of 535 questions.
Our goal was to cover a variety of research areas, so each respondent was exposed to certain sections but not others based on their previous questions. For example, questions about Go were shown only to programmers who use Go. In addition, we randomized questions and sections to further reduce the load on each respondent.
On average, participants spent 30 minutes completing the survey. While we have made efforts to streamline the process, we aim to make it even more efficient next year.
We invited potential respondents by using X ads, Facebook ads, Instagram, Reddit, Quora, BiliBili, Zhihu, IT Media, and JetBrains’ own communication channels. We also posted links to user groups and tech community channels and asked respondents to share the survey link with their peers.
This year, we advertised using unbranded creatives. This significantly reduced the positive bias toward JetBrains’ product users and yielded more representative data, but it also led to some discrepancies with data from previous years.
We collected sufficiently large samples from 20 geographical regions. The 11 countries with the most developers – Brazil, Canada, China, France, Germany, India, Japan, South Korea, Spain, the United Kingdom, and the United States – formed their own individual regions. The remaining countries were grouped into nine additional regions as follows:
- Africa
- Benelux and Northern Europe
- Central Europe
- Central and South America (excluding Brazil)
- Eastern Europe, the Balkans, and the Caucasus
- Middle East and Central Asia
- Gulf Countries
- Northern and Eastern Mediterranean (including Cyprus, Israel, and Portugal)
- Southeast Asia and Oceania (including Australia and New Zealand)
For each region, we collected at least 300 responses from external sources, such as ads or respondents’ referrals.
We weighted responses by their source. As our baseline dataset, we used the responses collected from external channels that are less biased toward JetBrains users, such as paid ads on X, Facebook, Instagram, Quora, and referrals. Then, for each respondent, we applied a three-stage weighting procedure to produce a more balanced view of the global developer population.
In the first stage, we assembled the responses collected while targeting different countries. Then, we applied our estimations of the populations of professional developers in each country to these data.
First, we took the survey data we received from professional developers and working students who were directed to us via ads posted on various social networks in the 20 regions, along with the data that we received from various peer referrals. Then, we weighted the responses according to our estimated populations of professional developers in those 20 regions. This ensured that the distribution of the responses corresponded to the population size of professional developers in each country.
In the second stage, we forced the proportion of students and unemployed respondents to be 17% in every country. We did this to maintain consistency with the previous year’s methodology, as that is the only estimate of their populations we have available.
By this point, we had a distribution of responses from external sources weighted both by region and employment status.
The third stage was rather sophisticated, as it included calculations obtained by solving systems of equations. We took those weighted responses, and for the developers from each region, in addition to their employment status, we calculated the shares for each of the 30+ programming languages, as well as the shares for those who answered “I currently use JetBrains products” and “I have never heard of JetBrains or its products”. Those shares became constants in our equations.
The next step was to add two more groups of responses from other sources: JetBrains internal communication channels, such as JetBrains social media accounts and our research panel, and social network ad campaigns targeted at users of specific programming languages.
We composed a system of 30+ linear equations and inequalities that described:
- Surveys that were filled out too fast.
- The weighting coefficients for the respondents (as a hypothetical example, Fiona from our sample represents, on average, 180 software developers from France).
- The specific values of their responses (for example, Pierre uses C++, is fully employed, and has never heard of JetBrains).
- The necessary ratios among the responses (for example, 27% of developers have used C++ in the past 12 months, and so on).
To solve this system of equations with the minimum variance of the weighting coefficients (which is essential!), we used the dual method of Goldfarb and Idnani (1982, 1983), which helped us to calculate the optimal individual weighting coefficients for all respondents.
Despite these measures, some bias is likely present, as JetBrains users might have been more willing, on average, to complete the survey. As in the previous year, we additionally corrected for this by reducing their representation in the dataset by 10%, i.e., multiplying their share of responses by 0.9.
As much as we try to control the survey distribution and apply smart weighting, the communities and the developer ecosystem are constantly evolving, and the possibility of some unexpected data fluctuations cannot be completely eliminated.
Raw data
Want to dig into the results yourself? Download the anonymized survey responses and see what you discover!
This report covers only the main highlights, but the raw data contains all 500+ survey questions.
We will continue to update and improve our methodology in the future. Stay tuned for the Developer Ecosystem Survey 2027!
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