Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation
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Abstract (arXiv)
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing code translation benchmarks primarily focus on individual functions, overlooking the complexities involved in translating entire repositories, such as maintaining inter-module coherence and managing dependencies. While some recent repository-level translation benchmarks attempt to address these challenges, they still face limitations, including poor maintainability and overly coarse evaluation granularity, which make them less developer-friendly. We introduce Skeleton-Guided-Translation, a framework for repository-level Java to C# code translation with fine-grained quality evaluation. It uses a two-step process: first translating the repository's structural "skeletons", then translating the full repository guided by these skeletons. Building on this, we present TRANSREPO-BENCH, a benchmark of high quality open-source Java repositories and their corresponding C# skeletons, including matching unit tests and build configurations. Our unit tests are fixed and can be applied across multiple or incremental translations without manual adjustments, enhancing automation and scalability in evaluations. Additionally, we develop fine-grained evaluation metrics that assess translation quality at the individual test case level, addressing traditional binary metrics' inability to distinguish when build failures cause all tests to fail. Evaluations using TRANSREPO-BENCH highlight key challenges and advance more accurate repository level code translation.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 27 Jan 2025.
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28 of 40 references.
References · 28
- 2017Attention Is All You Needcited by 60,361
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
- 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 523
- 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 479
- 2020Unsupervised Translation of Programming Languagescited by 263
- 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 200
- 2015Multi-task Sequence to Sequence Learningcited by 94
- 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
16 more
- 2021AVATAR: A Parallel Corpus for Java-Python Program Translationcited by 59
- 2023CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translationcited by 57
- 2023xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrievalcited by 47
- 2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
- 2022StructCoder: Structure-Aware Transformer for Code Generationcited by 34
- 2023On the Evaluation of Neural Code Translation: Taxonomy and Benchmarkcited by 23
- 2024RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translationcited by 23
- 2023CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Executioncited by 22
- 2024VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learnerscited by 20
- 2024Rectifier: Code Translation with Corrector via LLMscited by 20
- 2024ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairscited by 19
- 2024Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?cited by 17
- 2024Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generationcited by 16
- 2024Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysiscited by 16
- 2023Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanationscited by 12
- 2023Automatically Testing Functional Properties of Code Translation Modelscited by 3
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.