Automated source code summarization via transformer
Source code summarization is a comprehensible description of a program’s functionality. The code summarization assists developers to understand large portions of source code, thus reducing the time taken to comprehend a program’s capabilities. To automate the code summarization, programs have used R...
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2021
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sg-ntu-dr.10356-1531882021-11-16T05:09:40Z Automated source code summarization via transformer Viswen Kumar Mariammalle Liu Yang School of Computer Science and Engineering yangliu@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing Source code summarization is a comprehensible description of a program’s functionality. The code summarization assists developers to understand large portions of source code, thus reducing the time taken to comprehend a program’s capabilities. To automate the code summarization, programs have used RNN-based neural architecture to create neural network models for this natural language translation. However, the RNN-based neural architecture has two particular limitations which are its disability to process the non-sequential structure of the source codes and missing out on the long-term relationships between code tokens. My proposed approach of using Transformer neural architecture is able to overcome these limitations. Compared against the RNN-based neural network models, the Transformer network model has shown significantly better experimental results of BLEU 1, 2, 3 and 4 scores, ranging between three to seven scores higher, METEOR score of three higher and ROUGE-L score of one higher. Bachelor of Engineering (Computer Science) 2021-11-16T01:11:53Z 2021-11-16T01:11:53Z 2021 Final Year Project (FYP) Viswen Kumar Mariammalle (2021). Automated source code summarization via transformer. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153188 https://hdl.handle.net/10356/153188 en SCSE20-0713 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Document and text processing Viswen Kumar Mariammalle Automated source code summarization via transformer |
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Source code summarization is a comprehensible description of a program’s functionality. The code summarization assists developers to understand large portions of source code, thus reducing the time taken to comprehend a program’s capabilities. To automate the code summarization, programs have used RNN-based neural architecture to create neural network models for this natural language translation. However, the RNN-based neural architecture has two particular limitations which are its disability to process the non-sequential structure of the source codes and missing out on the long-term relationships between code tokens. My proposed approach of using Transformer neural architecture is able to overcome these limitations. Compared against the RNN-based neural network models, the Transformer network model has shown significantly better experimental results of BLEU 1, 2, 3 and 4 scores, ranging between three to seven scores higher, METEOR score of three higher and ROUGE-L score of one higher. |
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Liu Yang |
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Liu Yang Viswen Kumar Mariammalle |
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Final Year Project |
author |
Viswen Kumar Mariammalle |
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Viswen Kumar Mariammalle |
title |
Automated source code summarization via transformer |
title_short |
Automated source code summarization via transformer |
title_full |
Automated source code summarization via transformer |
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Automated source code summarization via transformer |
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Automated source code summarization via transformer |
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automated source code summarization via transformer |
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Nanyang Technological University |
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2021 |
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https://hdl.handle.net/10356/153188 |
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1718368070087475200 |