A Fully Automated Agent for End-to-End Code Translation and Validation
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".