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What is Facebook’s TransCoder AI?

  • Neelam Tyagi
  • Aug 06, 2020
  • Updated on: Jun 11, 2021
What is Facebook’s TransCoder AI? title banner

The voyages of code flow amid languages is such as up-scaleable and difficult task, one should be adroit in order to transcribe from one language to another at both level. Presently available tools are utterly automated, they generate unreadably and complex codes. 


For example, the translation of code from an antiquated programing language like COBOL to an advanced language like Java, Python, and C++ is a difficult task, certainly. Therefore, it demands expertise in the source and target language both.


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COBOL is still broadly adopted in the board system across the world, consequently, hundreds of companies, govt organizations, and other management sources need to prefer either to interpret their codebases manually or execute to continue code transcripted in an old-fashioned language.  


Following the specification of an automated language-translater tool, we are discussing the TransCoder, launched by Facebook AI, through this blog. 


“Drawing the world unitedly by introducing advanced AI” 


Superficially, unsupervised neural machine translation is implemented to the authorized code of Python, Java, and C++. It is capable to translate among them even without being trained in a supervised manner. 





"A transcompiler, or transpiler or source-to-source compiler is a language translator that converts code and functions among programming languages. Transcompilers are hugely different from conventional compilers that interpret source code from a high-level language to low-level programming language."


Priorly, transcompilers were designed to transport code-source among various platforms, for example, converting the code-source that is designed for Intel 8080 processor to making it congenial with the Intel 8086.


Through the same race, more advanced languages were developed like CoffeeScript, TypeScript, Dart, Haxe alongside transcompilers to convert them into a famous language like JavaScript. Conflicting conventional programming languages, these new languages translate with good accuracy. 


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Fundamentally, transcompilers are adopted for inter-operability, to port code scripted in the antiquated language to a recent language, that is relying on handcrafted rewrite rules.


Therefore, resulting translations lack readability and demand for manual corrections for working correctly. This is a time-consuming task and necessity expert knowledge in both source and target languages to make the code executable, but again much expensive.


Although neural models defeat their rule-based analogs in terms of natural language translation, one of them is “Facebook's TransCoder AI” which is the center of study throughout this blog. 


Understanding Facebook's TransCoder AI


According to the Facebook AI, A fully self-supervised neural transcompiler system, known as the “TransCoder”, is developed that can compose code voyage at a more permissive and cost-effective level.


This is the very first initiative based on an AI system that can explicate one programming code language into another programming language without looking at parallel data for training externally. As discussing programming, learn more about introdcution to R programming.


The TransCoder is trained on a public Github corpus repository that comprises more than 2.8 million open-source repositories, focusing translation at the function level.


While experimenting and demonstrating with TransCoder, it can be observed that TransCoder is capable to translate functions and code amidst C++, Java, and Python 3 favorably. It can outrun open-sourced and industrial rule-based interpretation programs.   


However, self-supervised training is specifically crucial for interpreting within various programming languages. Priorly, conventional supervised learning methods (such as LDA) depend upon extensive parallel datasets for training explicitly.   


Model-Making For Programming Languages


From the paper published, In natural language, most latest advanced researches have taken places that are accepted widely in neural machine translation, even professional translators also rely on automated machine translation tools actively. 


But still, programmers depend on the rule-based code translators that lack expert knowledge to review and debug the output, or simply to translate code manually. Now the relieving part is, “TransCoder surmounts these hurdles by supporting the latest progress in unsupervised machine translation to programming languages”. 


A sequence-to-sequence(seq2seq) model is built that incorporates an encoder and a decoder with a transformer architecture. The model gets trained on the three principles of unsupervised machine translation, i.e “ initialization, language modeling, and back translation”. Briefing them below 

This image presents how Facebook TransCoder influences the main three principles of unsupervised machine translation.

Cross-lingual masked model-pretraining, Image source

  1. First, the model gets trained on input sequences to have random tokens masked, i.e., the model necessity learns how to predict the true value for the masked tokens. 

  2. Next, the model is trained over sequences that have been corrupted by random masking, rearranging, or eliminating tokens, i.e., the model acquires to provide output in the context of the corrected sequence. 

  3. Finally, two versions of these models are trained simultaneously to perform back-translation; where one model determines to interpret from the source to target language, and the other model grasps to elucidate back to the source. (reference is taken from


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What Are Its Salient Specifications?

  1. During the evaluation, the model can translate more than 90% of the Java functional code to C++, 74.8% of C++ functions to Java, and more around 68.7% of functions to Python from Java

  2. While correlating with industrial available automated-tools that can able to translate up to 61.0% of functions precisely from C++ to Java and open-sourced translator is correct for up to 38.3% of Java functions interpreted into C++.

  3. TransCoder commits broadly on source code that is transcripted in one single programming language, instead of needing examples of the alike codes in terms of both source and target languages. 

  4. It is far comfortable to generalize the approach of TransCoder to extra programming languages, and it doesn’t demand expertise in the programming languages. 

  5. The transCoder will be helpful to upgrade conventional codebases to modern programming languages that are more economical and simpler to sustain. It also reflects how could neural machine translation techniques be implemented in novel specialties.  


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In order to evaluate the performance of TransCoder and other translation approaches, a matric is created, named computational accuracy. It checks whether the hypothesis function produces the equivalent outcome as generated by the reference when provided them identical input. 


Below is the picture of the test set and scripts and unit tests that are used to measure the metric.

The image shows how TransCoder translates from Python code(input) to C++ unsupervised machine translation.

Translating sample code from Python to C++, Source 

The TransCoder is capable enough to translate the Python input function “SumOfKsubArray” into C++ unsupervised translation favorably. It also indicates various types of “the arguments”, “return type”, and “parameters of the function”. The model can conjoin the Python “dequeue()” container to the C++ application “dequeue<>”.



It can be concluded that unsupervised machine translation methods can be implemented to the source-code in order to make the transcompiler in an entirely unsupervised style. 


From the VentureBeat,

“TransCoder could be generalized with any of the programming languages, it doesn’t demand any expertise and outruns industrial available tools through a huge edge.”


Also, the coauthors of the paper stated, “the model can easily fix a lot of mistakes by appending easy constraints to the decoder, it makes sure that composed functional codes are syntactically true.”


Moreover, taking into account the compiler outcome or other significant approaches like iterative error correction would enhance performance and achievement.

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