Author(s)

ZhengLin Wang

Affiliation(s)

School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, Hebei, China.

Corresponding Author

ZhengLin Wang

ABSTRACT

Learning Web application design and development requires a deep understanding of the industry’s technological ecosystem. To explore the core position and evolution trend of the Java language in the current computer software field, this paper systematically surveys and analyzes Java’s domestic and international rankings, market share, talent demand structure, and future development direction based on historical data from authoritative international programming language rankings such as TIOBE and PYPL, combined with big data from major domestic recruitment platforms such as Boss Zhipin and Lagou.com. The survey results show that despite challenges from Python in the field of artificial intelligence and Go in the field of cloud-native infrastructure, Java remains firmly in the top tier of global programming languages due to its over 33% market share in enterprise-level server applications and its dominant position in big data processing and Android mobile development. The key innovations of this study lie in: 1) constructing a multi-dimensional analytical framework that integrates technological rankings, market penetration, and talent analytics to assess programming language ecosystems; 2) revealing the structural shift in Java talent demand from basic coding to composite competencies in architecture, cloud-native, and AI engineering. Regarding talent demand, the market exhibits significant structural changes: the demand for entry-level CRUD (Create, Read, Update, Delete) positions is shrinking, while there is a shortage of advanced, multi-skilled talents with microservice architecture, cloud-native technologies, and JVM low-level optimization capabilities. Furthermore, this paper also discusses the profound impact of GraalVM native image technology and the engineering implementation of Spring AI on the future Java ecosystem.

KEYWORDS

Java ecosystem; Enterprise software; Talent analytics; Cloud-native transition; Programming language trends

CITE THIS PAPER

ZhengLin Wang. Market-driven analysis of Java ecosystem evolution and talent demand dynamics. World Journal of Information Technology. 2025, 3(6): 29-35. DOI: https://doi.org/10.61784/wjit3073.

REFERENCES

[1] Yangyang S, Zhu Jun W, Muhammad D, et al. The best publishing strategy of enterprise software companies facing the competition of cloud service providers. Expert system and application, 2024, 236.

[2] Andriol J S. Editorial: Where has IT gone?. International Journal of Technology Management, 2022, 89(1-2): 1-8.

[3] ZHANG Qi-Xun, WU Yi-Fan, YANG Yong, et al. Survey on service dependency discovery technologies for microservice systems. Journal of Software, 2024, 35(1): 118-135.

[4] DONG Hao-Wen, ZHANG Chao, LI Guo-Liang, et al. Survey on cloud-native databases. Journal of Software, 2024, 35(2): 899-926.

[5] Russell F. The combination of information technology and decentralized workplace organization: small and medium-sized enterprises and large enterprises. International business economics Journal, 2016, 23(2): 199-241.

[6] Peng Y, Hao J, Chen Y. Performance prediction and resource adaptive adjustment for cloud-native microservices. Cluster Computing, 2025, 28(12): 786.

[7] Hays. 2025 Hays Asia Salary Guide. Hays Specialist Recruitment, 2025.

[8] Maximilian S, Manuel W, Helmut K. Capabilities for value co-creation and value capture in emergent platform ecosystems: A longitudinal case study of SAP’s cloud platform. Journal of Information Technology, 2021, 36(4): 365-390.

[9] DI MEGLIO S, STARACE L L L. Evaluating performance and resource consumption of REST frameworks and execution environments: Insights and guidelines for developers and companies. IEEE Access, 2024, 12, 161649-161669.  

[10] LASIC L, BERONIC D, MIHALJEVIC B, et al. Assessing the efficiency of Java virtual threads in database-driven server applications//Proceedings of the 2024 47th MIPRO ICT and Electronics Convention (MIPRO). Opatija, IEEE, 2024, 926-931.

[11] Rana A. AI-Driven CRM Automation: Cloud-Native Architectures for Omnichannel Customer Experience Optimization. Journal of Computer Science and Technology Studies, 2025, 7(9): 9-17.