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Developer 5 min read Published Updated Credibility 94/100

Stack Overflow Survey

Stack Overflow's 2025 Developer Survey results are out. AI coding assistants are now used by the majority of professional developers, Rust continues to be the most loved language, and remote work remains the preference. Use these benchmarks for your technology strategy discussions.

Kodi C.

Editor & Research Lead

Verified for technical accuracy — Kodi C.

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Developer enablement and platform engineering briefings

Stack Overflow published the 2025 Developer Survey on June 20, 2025, aggregating responses from 86,000 developers across 185 countries. The survey shows Python overtaking JavaScript as the most commonly used language (59% of respondents) and 82% of professional developers integrating AI assistants into workflows. GitHub’s Octoverse 2024 report corroborates the trend, noting a 65% year-over-year increase in AI-assisted pull requests and rapid adoption of Rust and Go in cloud-native repos.

What the industry is signaling

  • Language shifts. Stack Overflow reports Python, JavaScript, and TypeScript as the top three languages, with Rust breaking into the top ten for the first time.
  • AI tooling mainstream. 54% of respondents cite productivity gains from AI code completion, while 42% raise concerns about security review debt.
  • Collaboration velocity. GitHub observed teams using code search and Copilot shipping 55% more pull requests per developer in 2024.

How controls apply

  • Secure SDLC. Update secure coding standards to cover Python and AI-assisted workflows, referencing OWASP Top 10 for LLM Applications.
  • Toolchain governance. Ensure AI coding assistants meet data-handling and auditability requirements before enabling in regulated repositories.

Detection checklist

  • Implement guardrails that scan AI-generated code for secret leakage, dependency risks, and insecure patterns before merge.
  • Monitor repository analytics for spikes in AI-assisted contributions that could signal review fatigue or quality drift.

Steps to take

  • Launch targeted enablement on Python, Rust, and AI tooling for platform and SRE teams to match adoption trends.
  • Capture survey metrics in developer experience scorecards shared with engineering leadership and HR to inform hiring and upskilling plans.

Cited sources

This brief arms platform teams with survey-backed priorities for language support, tooling governance, and AI adoption.

Development recommendations

Development teams should adopt practices that ensure code quality and maintainability during and after this transition:

  • Code review focus areas: Update code review checklists to include checks for deprecated patterns, new API usage, and migration-specific concerns. Establish review guidelines for changes that span multiple components.
  • Documentation updates: Ensure README files, API documentation, and architectural decision records reflect the changes. Document rationale for setup choices to aid future maintenance.
  • Version control practices: Use feature branches and semantic versioning to manage the transition. Tag releases clearly and maintain changelogs that highlight breaking changes and migration steps.
  • Dependency management: Lock dependency versions during migration to ensure reproducible builds. Update package managers and lockfiles systematically to avoid version conflicts.
  • Technical debt tracking: Document any temporary workarounds or deferred improvements introduced during migration. Create backlog items for post-migration cleanup and improvement.

Consistent application of development practices reduces risk and accelerates delivery of reliable software.

Long-run considerations

If you are affected, plan for ongoing maintenance and evolution of systems affected by this change:

  • Support lifecycle awareness: Track support timelines for dependencies, runtimes, and platforms. Plan upgrades before end-of-life dates to maintain security patch coverage.
  • Continuous improvement: Establish feedback loops to identify improvement opportunities. Monitor performance metrics and user feedback to guide iterative improvements.
  • Knowledge management: Build team expertise through training, documentation, and knowledge sharing. Ensure institutional knowledge is preserved as team composition changes.
  • Upgrade pathways: Maintain awareness of future versions and breaking changes. Plan incremental upgrades rather than large leap migrations where possible.
  • Community engagement: Participate in relevant open source communities, user groups, or vendor programs. Stay informed about roadmaps, good practices, and common pitfalls.

preventive maintenance planning reduces technical debt accumulation and ensures systems remain secure, performant, and aligned with business needs.

  • Test coverage analysis: Review existing test suites to identify gaps in coverage for affected functionality. Prioritize test creation for high-risk areas and critical user journeys.
  • Regression testing: Establish full regression test suites to catch unintended side effects. Automate regression runs in CI/CD pipelines to catch issues early.
  • Performance testing: Conduct load and stress testing to validate system behavior under production-like conditions. Establish performance baselines and monitor for degradation.
  • Security testing: Include security-focused testing such as SAST, DAST, and dependency scanning. Address identified vulnerabilities before production deployment.
  • User acceptance testing: Engage teams in UAT to validate that changes meet business requirements. Document acceptance criteria and sign-off procedures.

A full testing strategy provides confidence in changes and reduces the risk of production incidents.

Collaboration guidance

Effective collaboration across teams ensures successful adoption and ongoing support:

  • Cross-functional alignment: Coordinate with product, design, QA, and operations teams on setup timelines and dependencies. Establish regular sync meetings during transition periods.
  • Communication channels: Create dedicated channels for questions, updates, and issue reporting related to this change. Ensure relevant teams are included in communications.
  • Knowledge sharing: Document lessons learned and share good practices across teams. Conduct tech talks or workshops to build collective understanding.
  • Escalation paths: Define clear escalation procedures for blocking issues. Ensure decision-makers are identified and available during critical phases.
  • Retrospectives: Schedule post-setup retrospectives to capture insights and improve future transitions. Track action items and follow through on improvements.

Strong collaboration practices accelerate delivery and improve outcomes across the organization.

See also

Selected articles on related subjects.

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Cited sources

  1. Stack Overflow: 2025 Developer Survey — survey.stackoverflow.co
  2. Stack Overflow: Developer profile data (2025) — survey.stackoverflow.co
  3. GitHub Octoverse 2024 report — octoverse.github.com
  • Stack Overflow Survey
  • Developer productivity
  • AI tooling
  • GitHub Octoverse
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