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Title: Automated source code summarization via transformer
Authors: Viswen Kumar Mariammalle
Keywords: Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Viswen Kumar Mariammalle (2021). Automated source code summarization via transformer. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE20-0713
Abstract: 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.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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