A new report on the state of Java development reveals that insufficient tooling and long redeployments are the biggest productivity barriers for 53% of Java developers, while the use of AI tools is growing amongst Java developers.
Perforce Software, a DevOps and Java dev tool company, this week released the results of its latest annual survey of the Java community, the Perforce 2025 Java Developer Productivity Report, which cited these issues and others.
Impact of AI
AI has had a clear impact on Java development, as only 12% of respondents said they don’t use AI tools for Java development and another 12% work at companies that don’t allow AI tools — this rises to 16% in enterprise environments.
Moreover, the report showed that the top AI tools for Java developers include ChatGPT (52%), as well as developer-specific AI tools including GitHub CoPilot (42%) and IDE integrated AI tools (25%). Enterprise respondents preferred development-specific AI tools, with GitHub CoPilot in the lead at 52% for this subset, the report said.
AI Use Cases for Java Devs
Regarding use cases, respondents said they were most likely to turn to AI tools for code completion (60%) and refactoring (39%). Error detection (30%), documentation generation (28%), debugging assistance (26%), and automated testing (21%) were also key use cases.
“AI coding assistants get better every month. Developers who tried AI a few months ago may think it’s annoying or gets in the way. My advice is to keep trying AI tools at least once a quarter,” said Rod Cope, CTO of Perforce Software, in a statement.
According to the study, “Developers can choose between AI plugins for their IDE of choice, like GitHub CoPilot, a built-in IDE assistant like the JetBrains AI assistant, or a new IDE that’s built with native AI integration, such as Cursor. It’s not enough to just use AI tools; your business needs to be using the right AI tools. And what’s right is constantly changing based on use case and algorithm changes. Today, that might be an agentic IDE like Windsurf, but tomorrow that could be something else entirely.”
Certainly, Java developers are actively taking advantage of AI for code completion, a use case and practice that has quickly become nom de rigor among all developers thanks to the tremendous strides made by GenAI transformer models in speeding up “time to code,” said Brad Shimmin, VP and practice lead for data management and analytics at The Futurum Group.
“However, what I find most interesting in this report by Perforce is the sizable percentage (39%) of developers that are turning to AI for more complex code management use cases like refactoring,” Shimmin told The New Stack. “Again this is a testament to both the quality of today’s code-savvy large language models (LLMs), which are now able to ingest a full code repository as a part of a user’s prompt. But it is also a strong signal that more needs to be done in the way of helping developers (and companies at large) build cleaner code that does not require heavy lifting down the road in support of future enhancements and business requirement changes.
“Can we turn our code bases over to AI not just for development but also for maintenance and refinement? Not today, except within more confined use cases. But tomorrow? I think with innovations like test-time reasoning and agentic, autonomous AI workflows, there is hope.”
Generally, AI tools are being adopted as developers face pressure to “do more with less” and 50 % of respondents have incorporated AI tools into their workflow. Yet, AI hasn’t solved all productivity challenges, as 53% still cite long redeploys and insufficient development tools as their biggest barriers.
Inflection Point
The data reveals that Java development is at an inflection point where AI adoption is becoming a strategic necessity rather than just a technical novelty, according to Cope.
Indeed, a recent survey by Azul Systems indicated that Java is an emerging language for developing AI applications.
“Java’s growing role in artificial intelligence is becoming increasingly evident, with 50% of organizations using Java to build AI functionality, surpassing both Python and JavaScript for AI development among Java-centric enterprises,” the report read.
Simon Ritter, deputy CTO at Azul Systems, told The New Stack that based on Azul research including a recent survey of Java developers, Java could encroach on Python’s lead in use for AI development within a year and a half.
“It’s well known in developer circles that Java is better for developing enterprise AI applications given better scalability and performance, but right now Python outpaces Java with its libraries and other infrastructure to support the development of AI,” Ritter said. “However, enterprises are realizing that Java is the better choice for enterprise-level deployments. We’re likely to see Java outpace Python within the next 18 months to 3 years.”
Shift to LTS Versions
Meanwhile there has been a massive shift to JDK versions with long-term support (LTS), as 61% of respondents saying they use Java 17 with 45% using Java 21. Also, the IDE landscape is changing, as IntelliJ IDEA leads the field (84%), but VS Code (31%) has overtaken Eclipse (28%) as the second most popular IDE for Java development.
Other Java Challenges
Additionally, the report indicated that other significant challenges for Java developers include insufficient documentation (41%), communication issues between teams (38%), mismanaged timelines (32%), and developer turnover (26%).
With documentation and communication challenges among the leading obstacles to developer productivity, of the six obstacles asked about, “insufficient developer tools” got the fewest votes (24%). “No wonder fewer companies are adding budget for this,” said Lawrence Hecht, TNS research director.
Meanwhile, redeploy times were also cited as a problem by 29%. Deployment times vary greatly, Hecht said. Redeployment to remote, containerized and cloud development environments are more than two times as likely to take 5-plus minutes as compared to those being pushed locally (52% vs 23%), he noted.
Adding Java Resources
In addition, fewer companies are adding Java development resources in 2025, the report said.
This is the “glass is half empty” viewpoint, but 52% still plan to add more Java developers this year, Hecht said.
Furthermore, in 2025, 51% of respondents said their companies plan to add Java developers in the coming year, 16% did not plan to add any developer headcount, and 32% were not sure, the report said.
Similarly, “respondents were asked if their companies planned to increase their developer tool budget for 2025: 34% said yes, while 21% said there would be no tool budget increase and 45% were unsure,” the report said. “That’s a sharp decline from 2024 results for the same question, where 60% of respondents said they had plans to add Java developers in the coming year, and 42% said they intended to increase their developer tool budget.”
Biggest Barrier
Moreover, while “AI assistants and the like might be stealing headlines at the moment, keep in mind that 53% of respondents said that long redeploys and insufficient development tools are their biggest barrier to productivity,” the report said.
Perforce surveyed 731 developers, team leads, managers, and executives who work in Java about their current Java development environments, plans for their team’s future, productivity challenges, and more.
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