# CodeTrans: Towards Cracking the Language of Silicon'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, Burkhard Rost  
**arXiv:** [2104.02443](https://arxiv.org/abs/2104.02443)  
**Date:** 2021-04-06  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2104.02443v2/

## Key points

- CodeTrans is an encoder-decoder transformer model for software engineering tasks
- Transformer models combined with transfer learning have potential in natural language processing
- The paper explores the effectiveness of encoder-decoder transformer models for six software engineering tasks and thirteen sub-tasks
- Different training strategies are investigated, including single-task learning, transfer learning, multi-task learning, and multi-task learning with fine-tuning
- CodeTrans outperforms state-of-the-art models on all evaluated tasks
- Pre-trained models of CodeTrans are publicly available to support future work in the software engineering domain
- The paper provides insights into applying transformer models to understand and process source code language for software engineering tasks
- CodeTrans demonstrates its capability to improve various aspects of software engineering processes using self-supervised deep learning and high-performance computing techniques
- Authors include Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, and Burkhard Rost.
- The research spans 28 pages and includes six tables and one figure.
- The paper falls under the categories of computer science (cs.SE), artificial intelligence (cs.AI), computational linguistics (cs.CL), machine learning (cs.LG), and programming languages (cs.PL).
- Overall, the research presents a promising direction for utilizing transformer models in understanding source code language and enhancing software engineering practices.

## Summary

CodeTrans is an encoder-decoder transformer model designed for tasks in the software engineering domain. It aims to understand and process source code language to facilitate the software engineering process, which is an under-researched area. The transformer model, particularly when combined with transfer learning, has shown great potential in natural language processing tasks. This paper explores the effectiveness of encoder-decoder transformer models for six software engineering tasks and thirteen sub-tasks. The study investigates different training strategies, including single-task learning, transfer learning, multi-task learning, and multi-task learning with fine-tuning. CodeTrans surpasses state-of-the-art models on all the tasks evaluated. To support future work in the software engineering domain, the authors have made their pre-trained models of CodeTrans publicly available. The paper provides valuable insights into how transformer models can be applied to crack software engineering tasks by understanding and processing source code language. By leveraging self-supervised deep learning and high-performance computing techniques, CodeTrans demonstrates its capability to improve various aspects of software engineering processes. The authors of this paper include Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes and Burkhard Rost. The research spans 28 pages and includes six tables and one figure. The paper falls under the categories of computer science (cs.SE), artificial intelligence (cs.AI), computational linguistics (cs.CL), machine learning (cs.LG) and programming languages (cs.PL). Overall this research presents a promising direction for utilizing transformer models in understanding source code language and enhancing software engineering practices.