AI's Impact on Java Development: Key Findings from the 2025 Perforce Report

AI tools are reshaping Java development, addressing challenges such as deployment latency and documentation gaps. A new Perforce report shows that more than 53% of Java developers cite insufficient tools and long redeployment times as major productivity barriers, while AI tool usage is on the rise.

AI's Impact

Only 12% of respondents never use AI tools for Java, and another 12% work at companies that prohibit AI tools (16% in enterprise settings). The most popular AI tools are ChatGPT (52%), GitHub Copilot (42%), and IDE‑integrated AI assistants (25%). Enterprise respondents favor developer‑focused AI tools, with GitHub Copilot leading at 52%.

AI Use Cases for Java Developers

Developers most frequently use AI for code completion (60%) and refactoring (39%). Other important use cases include error detection (30%), documentation generation (28%), debugging assistance (26%), and automated testing (21%).

Perforce CTO Rod Cope notes that AI coding assistants improve each month and recommends trying AI tools at least quarterly.

Developers can add AI plugins to their IDEs—GitHub Copilot, JetBrains AI Assistant, or new AI‑enabled IDEs like Cursor—but merely using AI is insufficient; enterprises must select the right tools for specific use cases.

Generative AI has become a must‑have skill for developers, accelerating coding time and enabling complex tasks such as repository‑wide refactoring.

Turning Point

Cope observes that Java development is at a strategic inflection point, with AI adoption becoming a necessity rather than a novelty. Azul Systems reports that Java is emerging as a language for AI applications, with 50% of organizations using Java for AI features—outpacing Python and JavaScript in Java‑centric enterprises.

Azul’s Simon Ritter predicts that Java could overtake Python in AI development within 18‑36 months, citing Java’s scalability and performance for enterprise AI workloads.

Shift to LTS Versions

61% of respondents use Java 17 and 45% use Java 21, indicating a strong move toward long‑term support (LTS) JDKs. IntelliJ IDEA remains the dominant IDE (84%), with VS Code surpassing Eclipse as the second most popular (31%).

Other Java Challenges

Additional challenges include insufficient documentation (41%), communication issues between teams (38%), poor schedule management (32%), and developer turnover (26%). Documentation and communication are the top obstacles, while tool insufficiency receives the fewest votes (24%).

Redeployment time is also a concern for 29% of respondents; remote, containerized, and cloud deployments take more than five minutes twice as often as local redeployments (52% vs 23%).

Increasing Java Development Resources

The report notes that companies plan to reduce Java hiring by 2025, yet 52% still intend to add more Java developers this year. Overall, 51% plan to increase Java staff in the next year, while 16% have no hiring plans and 32% are uncertain.

Budget for development tools shows a decline: 34% will increase tool budgets, 21% will not, and 45% are unsure, down from 60% planning to increase staffing and 42% budgeting for tools in 2024.

Largest Obstacles

Despite AI’s headline appeal, 53% of respondents still cite long redeployment times and insufficient development tools as the biggest productivity hurdles.

The findings are based on a survey of 731 Java developers, team leads, managers, and executives, offering valuable insights for the developer community.

Author: Listening Music Fish Reference: https://www.jrebel.com/resources/java-developer-productivity-report-2025