This study focuses on the application and effectiveness evaluation of artificial intelligence agents (AI agents) in software code automatic migration scenarios. In response to the complex legacy system technology debt, high manual migration costs, and cross language/cross platform semantic mapping challenges faced by the US software industry, a four stage intelligent migration framework based on large-scale language models (LLMs) was constructed, which includes "understanding transformation verification optimization". The research adopts a multi-agent collaborative architecture, where syntax tree construction and core logic extraction are completed through code parsing agents. Semantic mapping agents rely on pre trained models to achieve cross language logic equivalence conversion, verify agent execution of compilation testing and logical consistency verification to ensure functional equivalence. In the optimization stage, reinforcement learning (RLHF) and context adaptive learning mechanisms are integrated to dynamically optimize transfer strategies. Introduce a human-machine collaborative verification model with a closed-loop mechanism of "proxy migration manual review feedback optimization" to ensure the interpretability and engineering controllability of the migration results. Empirical research on open source projects such as OpenStack and TensorFlow in the United States has shown that AI agent migration significantly outperforms traditional methods in accuracy, execution performance, and manual review efficiency, with an average efficiency improvement of 80%; Through the quantitative evaluation of multidimensional indicators such as migration accuracy, maintainability, and compliance, the framework demonstrates high migration accuracy (an average improvement of 25%), low error rate, and strong generalization ability. This framework forms an extensible technology path and standardized evaluation method, providing a complete theoretical and practical solution for intelligent migration of heterogeneous systems, supporting the upgrading of enterprise technology stacks and sustainable promotion of digital transformation, and verifying the core value of AI agents in software modernization.

Ziyi Song. Research on the Application and Effect Evaluation of AI Agent in Automated Software Code Migration. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 2: 165-170. https://doi.org/10.38007/IJBDIT.2025.060218.

[1] Chen, Y., Li, Z., & Xu, K. (2023). Automating legacy system modernization using large language models. IEEE Transactions on Software Engineering, 49(12), 6401–6415.

[2] Zhang, T., Liu, Y., & Wang, J. (2024). AI agents for code transformation: A multi-agent reinforcement learning framework. ACM Transactions on Intelligent Systems and Technology, 15(3), 1–22.

[3] Smith, A., & Johnson, M. (2025). Evaluating AI-driven code migration across heterogeneous software architectures. Journal of Systems and Software, 210, 112055.

[4] Kumar, R., & Patel, S. (2023). Contextual adaptation for LLM-based code translation. Proceedings of the 45th International Conference on Software Engineering (ICSE 2023), 1203–1214.

[5] Ding, J. (2025). Research On CODP Localization Decision Model Of Automotive Supply Chain Based On Delayed Manufacturing Strategy. arXiv preprint arXiv:2511.05899.

[6] Wu Y. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization[J]. 2025.

[7] Wu Y. Optimization of Generative AI Intelligent Interaction System Based on Adversarial Attack Defense and Content Controllable Generation[J]. 2025.

[8] Sun J. Quantile Regression Study on the Impact of Investor Sentiment on Financial Credit from the Perspective of Behavioral Finance[J]. 2025.

[9] Wang Y. Application of Data Completion and Full Lifecycle Cost Optimization Integrating Artificial Intelligence in Supply Chain[J]. 2025.

[10] Chen M. Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing[J]. 2025.

[11] Ding, J. (2025). Intelligent Sensor and System Integration Optimization of Auto Drive System. International Journal of Engineering Advances, 2(3), 124-130.

[12] Liu, X. (2025). Research on Real-Time User Feedback Acceleration Mechanism Based on Genai Chatbot. International Journal of Engineering Advances, 2(3), 109-116.

[13] Zhang, M. (2025). Research on Collaborative Development Mode of C# And Python in Medical Device Software Development. Journal of Computer, Signal, and System Research, 2(7), 25-32.

[14] Wang, Y. (2025). Intervention Research and Optimization Strategies for Neuromuscular Function Degeneration in the Context of Aging. Journal of Computer, Signal, and System Research, 2(7), 14-24.

[15] Shen, D. (2025). Construction And Optimization Of AI-Based Real-Time Clinical Decision Support System. Journal of Computer, Signal, and System Research, 2(7), 7-13.

[16] Hu, Q. (2025). The Practice and Challenges of Tax Technology Optimization in the Government Tax System. Financial Economics Insights, 2(1), 118-124.

[17] Sheng, C. (2025). Analysis of the Application of Fintech in Corporate Financial Decision-Making and Its Development Prospects. Financial Economics Insights, 2(1), 125-130.

[18] Wei, X. (2025). Deployment of Natural Language Processing Technology as a Service and Front-End Visualization. International Journal of Engineering Advances, 2(3), 117-123.