Using machine translation for converting Python 2 to Python 3 code

Aggarwal, Karan; Salameh, Mohammad; Hindle, Abram

doi:10.7287/peerj.preprints.1459v1

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Programming Language Translation Translating programs or source code across different pro-gramming languages (PL) requires a profound understanding of the PLs. Having strictly defined syntax and semantics, PLs are suitable for phrasebased statistical machine translation (Nguyen et al, 2013;Karaivanov et al, 2014;Aggarwal et al, 2015). Chen et al ( 2018) introduced a tree-to-tree machine translation to translate programs and to learn the syntactic alignment between source and target PL.…”

Section: Related Workmentioning

confidence: 99%

Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

Ahmad,

Chakraborty,

Ray

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled with a target-to-source model trained in parallel. The target-to-source model generates noisy sources, while the source-to-target model is trained to reconstruct the targets and vice versa. Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been very effective for a broad spectrum of downstream software engineering tasks. Hence, training them to build programming language translation systems via back-translation is compelling. However, these models cannot be further trained via back-translation since they learn to output sequences in the same language as the inputs during pre-training. As an alternative, we propose performing back-translation via code summarization and generation. In code summarization, a model learns to generate natural language (NL) summaries given code snippets. In code generation, the model learns to do the opposite. Therefore, target-to-source generation in back-translation can be viewed as a target-to-NL-to-source generation. We show that our proposed approach performs competitively with state-of-the-art methods. We have made the code publicly available. 1

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Section: Related Workmentioning

confidence: 99%

Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

Ahmad,

Chakraborty,

Ray

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…Initially, the phrase-based statistical machine translation model was primarily used for program translation between Java and C# (Nguyen et al 2013;Karaivanov et al 2014). Aggarwal et al (2015) used a similar approach to train a translation model from python2 to python3 on a parallel corpus generated by the open-source library 2 to 3. 1 Later, with the development of deep learning, research on program code translation based on the Tree2Tree model emerged.…”

Section: Translation Of Programming Languagesmentioning

confidence: 99%

HMPT: a human–machine cooperative program translation method

Zhang,

Yu,

Liu

et al. 2023

Autom Softw Eng

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Program translation aims to translate one kind of programming language to another, e.g., from Python to Java. Due to the inefficiency of translation rules construction with pure human effort (software engineer) and the low quality of machine translation results with pure machine effort, it is suggested to implement program translation in a human–machine cooperative way. However, existing human–machine program translation methods fail to utilize the human’s ability effectively, which require human to post-edit the results (i.e., statically modified directly on the model generated code). To solve this problem, we propose HMPT (Human-Machine Program Translation), a novel method that achieves program translation based on human–machine cooperation. It can (1) reduce the human effort by introducing a prefix-based interactive protocol that feeds the human’s edit into the model as the prefix and regenerates better output code, and (2) reduce the interactive response time resulted by excessive program length in the regeneration process from two aspects: avoiding duplicate prefix generation with cache attention information, as well as reducing invalid suffix generation by splicing the suffix of the results. The experiments are conducted on two real datasets. Results show compared to the baselines, our method reduces the human effort up to 73.5% at the token level and reduces the response time up to 76.1%.

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“…Early program translation models mainly conducted supervised learning on large bilingual corpora. Aggarwal et al [21] used the python2 to python3 parallel corpora of the post-open source code library 2to3 1 for supervised training. Anh et al [22] used a phrase-based statistical machine translation model to conduct supervised learning training for languages from Java to C#.…”

Section: Supervised Program Translationmentioning

confidence: 99%

Human-Machine Cooperative Program Translation Based on Abstract Syntax Tree

Zhang,

Liu,

et al. 2023

Preprint

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Program translation involves converting one programming language into another, which can modernize and migrate applications across platforms. Most of the current program translation methods are sequence-based, which cannot capture the semantic information of the code. To overcome this, abstract syntax tree-based program translation rises up. However, even the state-of-the-art machine translation methods can not guarantee error-free translation results. Consequently, software engineers need to make corrections on the machine's results before using, which is labor-intensive. To solve this problem, we propose the Human-Machine Cooperative Program Translation Based on Abstract Syntax Tree (HMPT-AST), which feeds the human's modification into the model in each iteration and the model updates the result based on human's feedback. It enables two patterns of human participation: First-error-based and Layer-based HMPT-AST. In the former one, the software engineer corrects one error node in each iteration. In the latter one, all error nodes within the same layer are corrected. In addition, the proposed method can also reduce the interactive response time from two aspects: avoiding generating the correct AST subtree repeatedly with parameter cache module, as well as reducing the generation of unnecessary AST subtree with early termination module. We conducted experiments on multiple dataset. The results show that compared with baselines, our method reduces the human effort up to 94.44\% and reduces the response time up to 94.32\%. We have made the code publicly available.\footnote{https://drive.google.com/drive/folders/1878Or6cEsW21fu_cpdpnv_dGevfFr9mB?usp=sharing}

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Using machine translation for converting Python 2 to Python 3 code

Cited by 11 publications

References 3 publications

Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

HMPT: a human–machine cooperative program translation method

Human-Machine Cooperative Program Translation Based on Abstract Syntax Tree

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