Translating to a Low-Resource Language with Compiler Feedback: A Case Study on Cangjie

In the rapidly advancing field of software development, the demand for practical code translation tools has surged, driven by the need for interoperability across different programming environments. Existing learning-based approaches often need help with low-resource programming languages that lack...

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Vydané v:IEEE transactions on software engineering Ročník 51; číslo 9; s. 2671 - 2692
Hlavní autori: Wang, Jun, Su, Chenghao, Ou, Yijie, Li, Yanhui, Tan, Jialiang, Chen, Lin, Zhou, Yuming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2025
IEEE Computer Society
Predmet:
Cangjie
Case studies
Codes
compiler feedback
Compilers
Computer languages
Data models
Datasets
Feedback
Java
Large language models
Learning
Programming environments
Programming languages
Reactive power
Software
Software development
Syntactics
Training
Translating
Translation
Transpiler
ISSN:0098-5589, 1939-3520
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Abstract In the rapidly advancing field of software development, the demand for practical code translation tools has surged, driven by the need for interoperability across different programming environments. Existing learning-based approaches often need help with low-resource programming languages that lack sufficient parallel code corpora for training. To address these limitations, we propose a novel training framework that begins with monolingual seed corpora, generating parallel datasets via back-translation and incorporating compiler feedback to optimize the translation model. As a case study, we apply our method to train a code translation model for a new-born low-resource programming language, Cangjie. We also construct a parallel test dataset for <inline-formula><tex-math notation="LaTeX">\mathsf{Java}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Java</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq1-3594908.gif"/> </inline-formula>-to-<inline-formula><tex-math notation="LaTeX">\mathsf{Cangjie}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Cangjie</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq2-3594908.gif"/> </inline-formula> translation and test cases to evaluate the effectiveness of our approach. Experimental results demonstrate that compiler feedback greatly enhances syntactical correctness, semantic accuracy, and test pass rates of the translated <inline-formula><tex-math notation="LaTeX">\mathsf{Cangjie}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Cangjie</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq3-3594908.gif"/> </inline-formula> code. These findings highlight the potential of our method to support code translation in low-resource settings, expanding the capabilities of learning-based models for programming languages with limited data availability.
AbstractList In the rapidly advancing field of software development, the demand for practical code translation tools has surged, driven by the need for interoperability across different programming environments. Existing learning-based approaches often need help with low-resource programming languages that lack sufficient parallel code corpora for training. To address these limitations, we propose a novel training framework that begins with monolingual seed corpora, generating parallel datasets via back-translation and incorporating compiler feedback to optimize the translation model. As a case study, we apply our method to train a code translation model for a new-born low-resource programming language, Cangjie. We also construct a parallel test dataset for <inline-formula><tex-math notation="LaTeX">\mathsf{Java}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Java</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq1-3594908.gif"/> </inline-formula>-to-<inline-formula><tex-math notation="LaTeX">\mathsf{Cangjie}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Cangjie</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq2-3594908.gif"/> </inline-formula> translation and test cases to evaluate the effectiveness of our approach. Experimental results demonstrate that compiler feedback greatly enhances syntactical correctness, semantic accuracy, and test pass rates of the translated <inline-formula><tex-math notation="LaTeX">\mathsf{Cangjie}</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Cangjie</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq3-3594908.gif"/> </inline-formula> code. These findings highlight the potential of our method to support code translation in low-resource settings, expanding the capabilities of learning-based models for programming languages with limited data availability.
In the rapidly advancing field of software development, the demand for practical code translation tools has surged, driven by the need for interoperability across different programming environments. Existing learning-based approaches often need help with low-resource programming languages that lack sufficient parallel code corpora for training. To address these limitations, we propose a novel training framework that begins with monolingual seed corpora, generating parallel datasets via back-translation and incorporating compiler feedback to optimize the translation model. As a case study, we apply our method to train a code translation model for a new-born low-resource programming language, Cangjie. We also construct a parallel test dataset for [Formula Omitted]-to-[Formula Omitted] translation and test cases to evaluate the effectiveness of our approach. Experimental results demonstrate that compiler feedback greatly enhances syntactical correctness, semantic accuracy, and test pass rates of the translated [Formula Omitted] code. These findings highlight the potential of our method to support code translation in low-resource settings, expanding the capabilities of learning-based models for programming languages with limited data availability.
Author Ou, Yijie
Zhou, Yuming
Li, Yanhui
Chen, Lin
Su, Chenghao
Wang, Jun
Tan, Jialiang
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10.18653/v1/2023.findings-emnlp.119
10.1145/1806799.1806848
10.18653/v1/2021.emnlp-main.685
10.1145/1806799.1806831
10.1109/ICSE48619.2023.00072
10.1145/3649506
10.18653/v1/p16-1009
10.1108/ws.2000.07949fab.004
10.18653/v1/2023.nlrse-1.10
10.1145/2591062.2591072
10.1007/978-3-031-44693-1_54
10.1075/target.8.1.04mar
10.18653/v1/D18-1399
10.1145/2491411.2494584
10.18653/v1/2024.acl-long.251
10.1145/3597503.3639226
10.18653/v1/2022.findings-acl.2
10.1145/2661136.2661148
10.18653/v1/2023.findings-emnlp.373
10.1109/eStream.2019.8732170
10.18653/v1/2023.findings-emnlp.337
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References Szafraniec (ref13) 2022
ref56
ref14
Achiam (ref22) 2023
(ref23) 2024
ref58
ref53
ref52
ref55
ref10
ref54
Hu (ref40) 2022
ref17
Liu (ref47) 2022
ref19
ref18
Kulal (ref35) 2019
Ren (ref37) 2020
Rozière (ref36) 2020
Gao (ref46) 2023
Nijkamp (ref16) 2022
ref50
ref45
ref48
ref44
(ref7) 2024
Touvron (ref20) 2023
ref49
ref9
Lample (ref27) 2017
Yang (ref39) 2024
Rafailov (ref33) 2023
(ref4) 2024
Roziere (ref12) 2020; 33
Dong (ref43) 2022
Lachaux (ref57) 2021; 34
Grubisic (ref59) 2024
Sanchez (ref6) 2024
Roziere (ref25) 2023
(ref38) 2024
Christopoulou (ref41) 2022
ref2
Chiang (ref30) 2025
ref1
Kaplan (ref51) 2020
(ref8) 2024
Ethayarajh (ref32) 2024
Smirnov (ref5) 2024
Chen (ref21) 2021
ref26
Rozière (ref24) 2022
Chen (ref60) 2023
(ref29) Apr. 21, 2025
(ref3) 2024
(ref34) 2024
ref28
Scao (ref15) 2022
Shen (ref42) 2023
(ref31) 2024
Chen (ref11) 2018; 31
ref61
References_xml – year: 2023
  ident: ref22
  article-title: Gpt-4 technical report
– volume: 34
  start-page: 14967
  volume-title: Proc. Adv. Neural Inf. Process. Syst.
  year: 2021
  ident: ref57
  article-title: DOBF: A deobfuscation pre-training objective for programming languages
– year: 2024
  ident: ref23
  article-title: Huawei Cangjie
– ident: ref9
  doi: 10.1109/ase.2015.74
– ident: ref54
  doi: 10.18653/v1/2023.findings-emnlp.119
– start-page: 1
  volume-title: Proc. 10th Int. Conf. Learn. Representations (ICLR)
  year: 2022
  ident: ref40
  article-title: LoRA: Low-rank adaptation of large language models
– year: 2024
  ident: ref4
  article-title: C2rust transpiler
– year: 2022
  ident: ref41
  article-title: PanGu-Coder: Program synthesis with function-level language modeling
– year: 2023
  ident: ref42
  article-title: PanGu-Coder2: Boosting large language models for code with ranking feedback
– year: 2022
  ident: ref43
  article-title: A survey on in-context learning
– start-page: 1950
  volume-title: Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)
  year: 2022
  ident: ref47
  article-title: Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
– ident: ref1
  doi: 10.1145/1806799.1806848
– year: 2021
  ident: ref21
  article-title: Evaluating large language models trained on code
– year: 2024
  ident: ref8
  article-title: Emscripten transpiler
– year: Apr. 21, 2025
  ident: ref29
  article-title: Text generation and prompting: Message roles and instruction following
– year: 2024
  ident: ref31
  article-title: Huawei Cangjie documents
– year: 2020
  ident: ref51
  article-title: Scaling laws for neural language models
– ident: ref17
  doi: 10.18653/v1/2021.emnlp-main.685
– year: 2023
  ident: ref46
  article-title: Human-like summarization evaluation with ChatGPT
– ident: ref2
  doi: 10.1145/1806799.1806831
– ident: ref14
  doi: 10.1109/ICSE48619.2023.00072
– year: 2023
  ident: ref60
  article-title: Teaching large language models to self-debug
– ident: ref44
  doi: 10.1145/3649506
– ident: ref26
  doi: 10.18653/v1/p16-1009
– year: 2024
  ident: ref5
  article-title: Cxgo transpiler
– year: 2022
  ident: ref15
  article-title: BLOOM: A 176b-parameter open-access multilingual language model
– year: 2025
  ident: ref30
  article-title: Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality
– ident: ref18
  doi: 10.1108/ws.2000.07949fab.004
– ident: ref49
  doi: 10.18653/v1/2023.nlrse-1.10
– start-page: 53728
  volume-title: Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)
  year: 2023
  ident: ref33
  article-title: Direct preference optimization: Your language model is secretly a reward model
– ident: ref52
  doi: 10.1145/2591062.2591072
– start-page: 20601
  volume-title: Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)
  year: 2020
  ident: ref36
  article-title: Unsupervised translation of programming languages
– ident: ref50
  doi: 10.1007/978-3-031-44693-1_54
– year: 2022
  ident: ref16
  article-title: CodeGen: An open large language model for code with multi-turn program synthesis
– ident: ref28
  doi: 10.1075/target.8.1.04mar
– year: 2017
  ident: ref27
  article-title: Unsupervised machine translation using monolingual corpora only
– year: 2024
  ident: ref3
  article-title: Facebook hack lang
– year: 2024
  ident: ref59
  article-title: Compiler generated feedback for large language models
– volume: 33
  volume-title: Proc. Adv. Neural Inf. Process. Syst.
  year: 2020
  ident: ref12
  article-title: Unsupervised translation of programming languages
– year: 2024
  ident: ref39
  article-title: Qwen2 technical report
– ident: ref56
  doi: 10.18653/v1/D18-1399
– year: 2024
  ident: ref34
  article-title: greengerong/leetcode at huggingface
– ident: ref10
  doi: 10.1145/2491411.2494584
– ident: ref61
  doi: 10.18653/v1/2024.acl-long.251
– year: 2024
  ident: ref6
  article-title: Sharpen transpiler
– start-page: 1
  volume-title: Proc. 10th Int. Conf. Learn. Representations (ICLR)
  year: 2022
  ident: ref24
  article-title: Leveraging automated unit tests for unsupervised code translation
– year: 2024
  ident: ref32
  article-title: KTO: model alignment as prospect theoretic optimization
– ident: ref55
  doi: 10.1145/3597503.3639226
– ident: ref58
  doi: 10.18653/v1/2022.findings-acl.2
– year: 2023
  ident: ref20
  article-title: Llama 2: Open foundation and fine-tuned chat models
– year: 2023
  ident: ref25
  article-title: Code Llama: Open foundation models for code
– year: 2024
  ident: ref7
  article-title: Java2csharp transpiler
– year: 2022
  ident: ref13
  article-title: Code translation with compiler representations
– start-page: 11883
  volume-title: Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)
  year: 2019
  ident: ref35
  article-title: SPoC: Search-based pseudocode to code
– ident: ref53
  doi: 10.1145/2661136.2661148
– year: 2024
  ident: ref38
  article-title: Huawei Cangjie specification
– ident: ref45
  doi: 10.18653/v1/2023.findings-emnlp.373
– volume: 31
  start-page: 2552
  volume-title: Proc. Adv. Neural Inf. Process. Syst.
  year: 2018
  ident: ref11
  article-title: Tree-to-tree neural networks for program translation
– ident: ref19
  doi: 10.1109/eStream.2019.8732170
– ident: ref48
  doi: 10.18653/v1/2023.findings-emnlp.337
– year: 2020
  ident: ref37
  article-title: CodeBLEU: A method for automatic evaluation of code synthesis
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SubjectTerms Cangjie
Case studies
Codes
compiler feedback
Compilers
Computer languages
Data models
Datasets
Feedback
Java
Large language models
Learning
Programming environments
Programming languages
Reactive power
Software
Software development
Syntactics
Training
Translating
Translation
Transpiler
Title Translating to a Low-Resource Language with Compiler Feedback: A Case Study on Cangjie
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