A Fully Automated Agent for End-to-End Code Translation and Validation
2025 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2025, Hawaii, Amerika Birleşik Devletleri, 2 - 03 Ekim 2025, ss.324-330, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/esem64174.2025.00058
- Basıldığı Şehir: Hawaii
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.324-330
- Anahtar Kelimeler: automated unit testing, code completion, code generation, code summarization, code translation
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Background: Software migration across programming languages is a critical yet labor-intensive task, often requiring deep code understanding and manual intervention. Aims: In this study, we aim to develop a fully automated agent for end-to-end code translation and validation. Method: First, we generate code comments from Java source code using various large language models (LLMs) to enhance code comprehension and facilitate cross-language translation. Second, leveraging these AI-generated comments, we automatically generate equivalent C# code, demonstrating the potential of AI in software migration and interoperability. Third, we complete both Java and generated C# code and prepare them to execute. Fourth, we apply automated unit testing to assess functional correctness and ensure the reliability of AI-generated code. Results: Our results show that a fully automated LLM agent may effectively bridge programming languages with minimal human input. This approach opens new possibilities for scalable, AIdriven software modernization and cross-platform development. Conclusions: We recommend that such an LLM agent should be used to support human experts during the generation of reliable and correct code.