CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing

• URL: http://arxiv.org/abs/2104.02443v1
• Date: Tue, 6 Apr 2021 11:57:12 GMT
• Title: CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing
• Authors: Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes and Burkhard Rost
• Abstract summary: This paper describes CodeTrans - an encoder-decoder transformer model for tasks in the software engineering domain. It explores the effectiveness of encoder-decoder transformer models for six software engineering tasks, including thirteen sub-tasks. CodeTrans outperforms the state-of-the-art models on all the tasks.
• Score: 4.111243115567736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applications for understanding source code language to ease the software engineering process are under-researched. Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks. These breakthroughs point out a promising direction for process source code and crack software engineering tasks. This paper describes CodeTrans - an encoder-decoder transformer model for tasks in the software engineering domain, that explores the effectiveness of encoder-decoder transformer models for six software engineering tasks, including thirteen sub-tasks. Moreover, we have investigated the effect of different training strategies, including single-task learning, transfer learning, multi-task learning, and multi-task learning with fine-tuning. CodeTrans outperforms the state-of-the-art models on all the tasks. To expedite future works in the software engineering domain, we have published our pre-trained models of CodeTrans. https://github.com/agemagician/CodeTrans

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