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DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Marie-Anne Lachaux · Baptiste Roziere · Marc Szafraniec · Guillaume Lample

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ None #None

Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks. However, research in language model pre-training has mostly focused on natural languages, and it is unclear whether models like BERT and its variants provide the best pre-training when applied to other modalities, such as source code. In this paper, we introduce a new pre-training objective, DOBF, that leverages the structural aspect of programming languages and pre-trains a model to recover the original version of obfuscated source code. We show that models pre-trained with DOBF significantly outperform existing approaches on multiple downstream tasks, providing relative improvements of up to 12.2% in unsupervised code translation, and 5.3% in natural language code search. Incidentally, we found that our pre-trained model is able to deobfuscate fully obfuscated source files, and to suggest descriptive variable names.

Author Information

Marie-Anne Lachaux (Facebook AI Research)
Baptiste Roziere (Facebook AI Research and Paris-Dauphine University)
Marc Szafraniec (Facebook AI Research)
Guillaume Lample (Facebook AI Research)

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