Lexical statistical machine translation for language migration

By Anh Tuan Nguyen, Tung Thanh Nguyen, Tien Nhut Nguyen. 2013

172 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Program Synthesis with Large Language Models (2021, methodology): “Porting programs across languages has been treated as a learning problem similar to machine translation [Roziere et al., 2020, Nguyen et al., 2013, Karaivanov et al., 2014].”
Unsupervised Translation of Programming Languages (2020, methodology): “The majority of studies in source code translation use the BLEU score to evaluate the quality of generated functions [1, 10, 22, 36], or other metrics based on the relative overlap between the tokens in the translation and in the reference.”
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow (2018, background): “Recent years have witnessed the burgeoning of a new suite of developer assistance tools based on natural language processing (NLP) techniques, for code completion [9], source code summarization [2], automatic documentation of source code [44], deobfuscation [16, 34, 40], cross-language porting [27, ”
GraphCodeBERT: Pre-training Code Representations with Data Flow (2020, methodology): “PBSMT is short for phrase-based statistical machine translation (Koehn et al., 2003), and has been exploited in previous works (Nguyen et al., 2013; Karaivanov et al., 2014).”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “[57–59] used statistical machine translation for method-to-method migration from Java to C#, translating small token sequences at a time.”
Tree-to-tree Neural Networks for Program Translation (2018, methodology): “Some recent work have applied statistical machine translation techniques to program translation [2, 16, 22, 21, 23, 24].”
Multi-lingual Evaluation of Code Generation Models (2022, background): “Earlier works (Nguyen et al., 2013; Karaivanov et al., 2014) focused on building semi-automatic tools to find similar functions in Java and C# from open source projects.”
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code (2020, background): “Precise scoring of code fragments is essential for tasks such as translating a program from one programming language to another [54, 66], code completion [32, 76], and code synthesis from natural language and vice versa [7, 27, 67, 73].”
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code (2019, background): “Also, improved language models for code have the potential to enable new tools for aiding code readability [2], program repair [11, 15, 35, 62], program synthesis [34] and translation be-tween programming languages [45, 56].”
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks (2021, background): “[7] ------ , “Lexical statistical machine translation for language migration,” in Proceedings o f the 2013 9th Joint M eeting on Foundations o f Software Engineering, ser.”
Phrase-Based Statistical Translation of Programming Languages (2014, background): “Programming languages and Statistical Methods The work most closely related to ours is Nguyen et al. [11] which uses a phrase-based SMT system to assist language migration.”
Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code (T) (2015, unknown): “Such divide-and-conquer and syntax-direction strategies enable phrase-based SMT to adapt well to syntactical structures in source code, thus, improving migration accuracy.”
Program Language Translation Using a Grammar-Driven Tree-to-Tree Model (2018, methodology): “Nguyen found SMT methods from NLP applied to programming language translation produced many syntactically incorrect programs (Nguyen et al., 2013).”
On Learning Meaningful Assert Statements for Unit Test Cases (2020, methodology): “has become an important tool for supporting software-related tasks such as bug-fixing [7, 12, 20, 36], code changes [35], code migration [21, 22], code summarization [18, 19, 39], pseudo-code generation [23], code deobfuscation [13, 38] and mutation analysis [37].”
Autofolding for Source Code Summarization (2014, background): “Recent work has applied language modelling [20], [21], [22], [23], [24], natural language generation [12], [25], machine translation [26], and topic modelling [27] to the text of source code”
Toward Deep Learning Software Repositories (2015, background): “human oracle cost [7], predicting programmer comments to improve search over code bases and code categorization [8], improving error reporting [10], generating feasible test cases to improve coverage [11], improving stylistic consistency to aid readability and maintainability [13], and code migratio”