Implementing Multi Lingual Capabilities for Software Platforms Static and Dynamic Translation Strategies
DOI:
https://doi.org/10.15662/IJEETR.2026.0801008Keywords:
Multi-lingual software platforms, Static translations, AI-powered translation, Language preference managementAbstract
The software platform that is going to spread all over the world should be available in several languages without compromising its performance and consistency. A significant technical issue is the control over the process of static translation and dynamic user-generated content. This paper introduces a hybrid multi-lingual translation model that is a combination of the datastore based static translations and AI based dynamic translation services. The suggested system employs language preference management, caching and decision logic in order to trade off between accuracy and response time. A quantitative comparison is made between the hybrid system and a translation strategy of the system that is static only in various languages including low-resource languages. They found that performance is better in the form of increased accuracy of translation, reduced fallback rates, and increased system performance. The results indicate that the hybrid translation structures are suitable to the contemporary global software platforms.
References
[1] Weigang, L., & Brom, P. C. (2025). LLM-BT-Terms: Back-Translation as a framework for terminology standardization and dynamic semantic embedding. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2506.08174
[2] Roy, S. (2025). AI-Powered Multi-Language Translation System: A Comprehensive Analysis of Features, Architecture, and Performance Metrics Institution: Advanced Language Technologies Laboratory. AI-Powered Multi-Language Translation System: A Comprehensive Analysis of Features, Architecture, and Performance Metrics Institution: Advanced Language Technologies Laboratory. https://doi.org/10.13140/rg.2.2.36231.05286
[3] Lano, K., & Siala, H. (2024). Using model-driven engineering to automate software language translation. Automated Software Engineering, 31(1). https://doi.org/10.1007/s10515-024-00419-y
[4] Agyei, E., Zhang, X., Quaye, A. B., Odeh, V. A., & Arhin, J. R. (2025). Dynamic aggregation and augmentation for Low-Resource machine translation using federated Fine-Tuning of pretrained transformer models. Applied Sciences, 15(8), 4494. https://doi.org/10.3390/app15084494
[5] Mayer, P., Kirsch, M., & Le, M. A. (2017). On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers. Journal of Software Engineering Research and Development, 5(1). https://doi.org/10.1186/s40411-017-0035-z
[6] Saulītis, A. (2025). Evaluating multilingual digital resources: machine translation adoption and user satisfaction across six European countries. Language Resources and Evaluation, 60(1). https://doi.org/10.1007/s10579-025-09884-7
[7] Seligman, M., & Waibel, A. (2019). Advances in Speech-to-Speech translation technologies. In Cambridge University Press eBooks (pp. 217–251). https://doi.org/10.1017/9781108525695.012
[8] Urlaub, P., & Dessein, E. (2022). Machine translation and foreign language education. Frontiers in Artificial Intelligence, 5, 936111. https://doi.org/10.3389/frai.2022.936111
[9] Karpava, S., Ringblom, N., & Zabrodskaja, A. (2025). Translanguaging as a dynamic strategy for heritage language transmission. Languages, 10(2), 19. https://doi.org/10.3390/languages10020019
[10] Sun, L., Wu, Y., Li, L., Zhang, C., & Tang, J. (2023). A dynamic and static binary translation method based on branch prediction. Electronics, 12(14), 3025. https://doi.org/10.3390/electronics12143025





