A recent survey by Perforce, a provider of Java development and DevOps tools, has revealed that while slightly over half (51%) of Java shops plan to increase their Java developer headcount in the coming year, this marks a decline from 60% in 2024. The data, presented in Perforce’s 2025 Java Developer Productivity Report, indicates a more cautious approach to hiring in the Java development space. This decline reflects broader industry trends, as companies face economic uncertainties and reassess their talent acquisition strategies. Despite this dip, Java continues to be a central language in enterprise development, and the need for skilled developers remains strong, albeit with more caution in recruitment.
In addition to slower hiring projections, the report also highlights a decrease in budget allocations for Java tools. While 34% of respondents plan to increase their Java tool budget in 2025, this is down from 42% in the previous year. This reduction suggests that while Java remains a critical part of many organizations’ technology stacks, businesses are scrutinizing their investments more closely. With the advent of new technologies and frameworks, companies may be prioritizing tools that support modern development practices, such as cloud computing and microservices, which require a broader skill set beyond traditional Java development.
The survey, which was conducted with 731 developers, team leads, managers, and executives working in Java, also explored the role of AI tools in Java development. Interestingly, only 12% of respondents reported that they do not use AI tools, and another 12% stated that their companies restrict the use of AI in development workflows. This shows that AI is becoming an increasingly integral part of the Java development process. Among the most popular AI tools, 52% of respondents said they use ChatGPT, while 42% use GitHub Copilot. IDE-integrated AI tools were also gaining traction, though they were used by a smaller proportion of developers (25%).
AI tools are being used primarily for code completion (60%), with refactoring (39%) and error detection (30%) also being common use cases. Other popular applications of AI in Java development include debugging assistance (26%), documentation generation (28%), and automated testing (21%). These findings reflect a growing reliance on AI to improve developer productivity and streamline the development process. However, as AI tools continue to evolve, companies must balance their integration with existing workflows while ensuring they maintain a clear understanding of how these tools impact code quality and developer workflows.