Software Development Statistics in 2026

A planning deck can go wrong before you finish the first slide. A global estimate of developers, a percentage from a voluntary survey, a GitHub activity count, and a U.S. employment projection may all be labelled “software development statistics,” even though they describe different populations.

The short answer: there is no single number that describes software development in 2026. The most useful figures separate analyst estimates, developer surveys, platform telemetry, delivery research, and government labor data. This report puts those boundaries next to the numbers so you can cite the figure that actually answers your question.

Key software development statistics for 2026

• 47.2 million developers worldwide in 2025 was SlashData’s estimate of the global developer population. It is an analyst estimate rather than a census. (SlashData, 2025)

• 27 million developers worldwide in 2024 was Evans Data’s estimate from its Worldwide Developer Population Report. Its period and population model differ from SlashData’s, so the two figures are not interchangeable. (Evans Data, 2024)

• More than 49,000 responses from 177 countries formed the respondent population for Stack Overflow’s 2025 Developer Survey. This is a survey sample, not a global developer count. (Stack Overflow, 2025)

• JavaScript was used by 66% of all Stack Overflow 2025 respondents, followed by HTML/CSS at 61.9%, SQL at 58.6%, Python at 57.9%, Bash/Shell at 48.7%, and TypeScript at 43.6%. Respondents could select more than one technology, so these are survey usage shares rather than market shares. (Stack Overflow technology results, 2025)

• Docker was used by 71.1% and AWS by 43.3% of all Stack Overflow 2025 respondents; Kubernetes, Azure, and Google Cloud were reported at 28.5%, 26.3%, and 24.6%, respectively. These are self-reported tool-usage figures, not cloud-provider market share. (Stack Overflow technology results, 2025)

• 84% of Stack Overflow 2025 respondents used or planned to use AI tools in their development process. That combined measure included 47.1% daily users, 17.7% weekly users, 13.7% monthly or infrequent users, and 5.3% who planned to use AI tools soon. (Stack Overflow, 2025)

• 85% of developers in JetBrains’ 2025 survey regularly used AI tools for coding and development, while 62% relied on at least one AI coding assistant, agent, or AI-enabled code editor. JetBrains surveyed 24,534 developers in 194 countries and regions from April through June 2025. (JetBrains, 2025)

• 90% of respondents in DORA’s 2025 research used AI at work, and more than 80% believed it improved their productivity. DORA’s nearly 5,000 technology-professional respondents are organizational research participants, not a census of developers. (Google Cloud / DORA, 2025)

• More than 36 million developers joined GitHub in the reported year, and more than 180 million developers worked and built on GitHub. These are GitHub platform counts, not worldwide developer-population estimates. (GitHub Octoverse, 2025)

• The median annual pay for U.S. software developers was $135,980 in May 2025. The U.S. Bureau of Labor Statistics also projected 1,905,400 jobs in 2025 and 10% growth from 2025 to 2035 for the combined U.S. occupation group of software developers, quality assurance analysts, and testers. (U.S. Bureau of Labor Statistics)

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How many developers are there worldwide?

The most defensible answer is a range of published estimates, not a number created by averaging them. SlashData estimated 47.2 million developers worldwide in 2025 and, within that total, 36.5 million professional developers in early 2025. It reported that its professional-developer estimate had grown from 21.8 million in early 2022. Those are analyst estimates with definitions that must stay attached to the figures. (SlashData, 2025)

Evans Data estimated 27 million developers worldwide in 2024. That is not evidence that one publisher is necessarily wrong. The sources use different periods, coverage, and population models. You should name the publisher and year whenever you use either estimate, rather than describing either as the definitive global headcount. (Evans Data, 2024)

This distinction prevents a common reporting error. A worldwide developer estimate is not the same as a count of people who responded to a survey, developers active on a code-hosting platform, or workers in a government occupation classification. Each can be useful, but each answers a different question.

Which languages and platforms do developers report using?

Stack Overflow’s 2025 survey offers a broad self-reported technology snapshot. Among all respondents, 66% reported using JavaScript, 61.9% HTML/CSS, 58.6% SQL, 57.9% Python, 48.7% Bash/Shell, and 43.6% TypeScript. Because the questionnaire allowed multiple selections, adding these percentages would be meaningless.

For infrastructure and cloud-related technologies, 71.1% of all respondents reported Docker use, 43.3% AWS, 28.5% Kubernetes, 26.3% Azure, and 24.6% Google Cloud. The same survey reported a 17-percentage-point year-over-year increase for Docker. These data describe what respondents said they use; they do not measure revenue, workloads, or provider market share. (Stack Overflow technology results, 2025)

Use this type of statistic when you need a view of developer-reported familiarity or usage. Do not use it to claim that a language owns a fixed share of all software projects. Survey respondents can use several languages, platforms, and tools in the same role.

What do the 2026 AI development statistics measure?

“AI adoption” is not one measure. Stack Overflow’s 84% captures respondents who either used AI tools or planned to use them in their development process in 2025. It should not be rewritten as “84% of developers use AI in production.” The survey’s daily-use figure was 47.1%, while 14.1% of respondents reported using AI agents at work daily.

Confidence points in a different direction from uptake. In Stack Overflow’s 2025 respondent sample, 46% distrusted the accuracy of AI tools, compared with 33% who trusted it. The coexistence of use and skepticism is more informative than either figure alone: tool presence does not establish confidence in its outputs. (Stack Overflow, 2025)

JetBrains measured a related but differently worded behavior. Its 2025 survey found 85% regular use of AI tools for coding and development and 62% reliance on at least one AI coding assistant, agent, or AI-enabled code editor. Its April-to-June 2025 fieldwork and 24,534-developer sample are not the same population or questionnaire as Stack Overflow’s, so the percentages should sit side by side rather than be combined. (JetBrains, 2025)

DORA examines AI through organizational delivery research. Its 2025 summary reports a positive relationship between AI adoption and software-delivery throughput and product performance, alongside a negative relationship with delivery stability. That is a reported relationship in its research population, not proof that AI causes the same outcome in every organization. (Google Cloud / DORA, 2025)

For a testing-specific view of where AI is being adopted, you can compare these broad developer measures with Quash’s separate AI testing statistics and adoption data. The questions differ: the sources above describe development-tool behavior, while testing data focuses on QA work.

What does GitHub activity reveal?

GitHub’s Octoverse data is platform telemetry. It records behavior on GitHub and is therefore valuable for questions about GitHub, but it does not represent every developer or every codebase. GitHub reported that more than 36 million developers joined its platform in the reported year and that more than 180 million developers worked and built on GitHub.

The same report identified more than 1.1 million public repositories using an LLM SDK. It also reported that 80% of new developers on GitHub used Copilot in their first week. The first statistic concerns public repositories using an LLM SDK; the second has a denominator of new GitHub developers. Neither is a global rate for all developers or all software organizations. (GitHub Octoverse, 2025)

That boundary matters when you use platform statistics in planning. GitHub can show a fast-moving signal of activity within its ecosystem. It cannot replace an analyst estimate of the global developer population or a survey of tools used beyond the platform.

What is the U.S. software developer labor-market outlook?

The BLS figure is specifically U.S. labor-market data. For software developers, BLS reported $135,980 median annual pay in May 2025. For the combined occupation group of software developers, quality assurance analysts, and testers, it listed 1,905,400 jobs in 2025 and projected 10% employment growth between 2025 and 2035.

Keep the occupation label intact. The pay statistic applies to software developers, while the employment and outlook figures apply to the broader combined occupation group. Neither statistic estimates global pay or worldwide employment. (U.S. Bureau of Labor Statistics)

If your staffing question includes test engineering, that broader group is more relevant than a developer-only title. For salary context focused on QA roles, see Quash’s QA engineer and SDET salary statistics. It is a separate labor-market question from the developer-population estimates above.

What does current delivery research say about AI?

DORA’s 2025 research sampled nearly 5,000 technology professionals. It found that 90% used AI at work, more than 80% believed AI increased productivity, and 30% reported little or no trust in AI-generated code. These are survey-based organizational research findings, not instrumented measures of code quality or universal rates for every software developer. (Google Cloud / DORA, 2025)

The delivery findings make the practical question more precise. You should not treat higher throughput as a substitute for stability, or treat a productivity perception as a direct measure of business outcomes. If you are deciding how to introduce AI-assisted development, monitor the outcome you care about—such as change stability, release speed, or defect escape rate—rather than relying on adoption alone.

Which popular software development statistics are excluded?

Several often-repeated figures do not meet the source standard for this report. The accessible current Standish Group page for its CHAOS report does not publish the commonly repeated project-success, challenged-project, and failed-project percentages in its visible product-page text. Those percentages are therefore not included as current evidence. (Standish Group)

This report also does not include a global software-development market-size total. A market figure should come from a directly accessible primary disclosure or clearly labelled original research with a defined market boundary. A number without that support can make a statistics roundup look comprehensive while making it harder to cite responsibly.

The omission is intentional: a shorter list of bounded figures is more useful than an attractive total with an unclear definition. If you need a market estimate for a budget decision, obtain the underlying research and preserve its geography, market scope, base year, and forecast period.

Methodology and source notes

This report groups evidence by what it measures:

1. Analyst population estimates: SlashData and Evans Data estimate worldwide developer populations with their own models.

2. Developer survey responses: Stack Overflow and JetBrains report answers from their respective respondent samples.

3. Platform telemetry: GitHub reports activity and adoption signals within GitHub.

4. Organizational delivery research: DORA reports relationships observed in its technology-professional research population.

5. Government labor-market data: BLS reports U.S. occupations, employment projections, and wages.

These categories are why similarly worded statistics can differ without being directly contradictory. A total developer estimate, a survey respondent share, and a platform activity count have different denominators. The right citation keeps the population, period, evidence type, and source next to the number.

No supplied first-party Quash data measures how broad software-development practices affect mobile-app quality among Quash users. Public sources in this report measure populations, reported tool use, platform activity, labor context, and delivery relationships; they do not supply a Quash-specific mobile-quality benchmark.

Conclusion

The most useful software development statistics for 2026 are the ones that preserve their boundaries. Use analyst estimates for global population context, survey data for reported technology behavior, GitHub data for GitHub activity, BLS data for U.S. labor questions, and DORA findings for organizational delivery relationships.

When you choose a figure for a plan, memo, or article, begin with the decision you need to make and then select the statistic with the matching population. That choice will make your conclusion more credible than any single headline number.