Bi-LSTM-Based Neural Source Code Summarization
Code summarization is a task that is often employed by software developers for fixing code or reusing code. Software documentation is essential when it comes to software maintenance. The highest cost in software development goes to maintenance because of the difficulty of code modification. To help...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/24/12587 |
| _version_ | 1797461617537974272 |
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| author | Sarah Aljumah Lamia Berriche |
| author_facet | Sarah Aljumah Lamia Berriche |
| author_sort | Sarah Aljumah |
| collection | DOAJ |
| description | Code summarization is a task that is often employed by software developers for fixing code or reusing code. Software documentation is essential when it comes to software maintenance. The highest cost in software development goes to maintenance because of the difficulty of code modification. To help in reducing the cost and time spent on software development and maintenance, we introduce an automated comment summarization and commenting technique using state-of-the-art techniques in summarization. We use deep neural networks, specifically bidirectional long short-term memory (Bi-LSTM), combined with an attention model to enhance performance. In this study, we propose two different scenarios: one that uses the code text and the structure of the code represented in an abstract syntax tree (AST) and another that uses only code text. We propose two encoder-based models for the first scenario that encodes the code text and the AST independently. Previous works have used different techniques in deep neural networks to generate comments. This study’s proposed methodologies scored higher than previous works based on the gated recurrent unit encoder. We conducted our experiment on a dataset of 2.1 million pairs of Java methods and comments. Additionally, we showed that the code structure is beneficial for methods’ signatures featuring unclear words. |
| first_indexed | 2024-03-09T17:22:55Z |
| format | Article |
| id | doaj.art-4ef46a1bfeba483dab3fd7bafc576b73 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:22:55Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4ef46a1bfeba483dab3fd7bafc576b732023-11-24T13:01:12ZengMDPI AGApplied Sciences2076-34172022-12-0112241258710.3390/app122412587Bi-LSTM-Based Neural Source Code SummarizationSarah Aljumah0Lamia Berriche1College of Computer & Information Sciences, Prince Sultan University, Riyadh 12435, Saudi ArabiaCollege of Computer & Information Sciences, Prince Sultan University, Riyadh 12435, Saudi ArabiaCode summarization is a task that is often employed by software developers for fixing code or reusing code. Software documentation is essential when it comes to software maintenance. The highest cost in software development goes to maintenance because of the difficulty of code modification. To help in reducing the cost and time spent on software development and maintenance, we introduce an automated comment summarization and commenting technique using state-of-the-art techniques in summarization. We use deep neural networks, specifically bidirectional long short-term memory (Bi-LSTM), combined with an attention model to enhance performance. In this study, we propose two different scenarios: one that uses the code text and the structure of the code represented in an abstract syntax tree (AST) and another that uses only code text. We propose two encoder-based models for the first scenario that encodes the code text and the AST independently. Previous works have used different techniques in deep neural networks to generate comments. This study’s proposed methodologies scored higher than previous works based on the gated recurrent unit encoder. We conducted our experiment on a dataset of 2.1 million pairs of Java methods and comments. Additionally, we showed that the code structure is beneficial for methods’ signatures featuring unclear words.https://www.mdpi.com/2076-3417/12/24/12587software engineeringneural networkcode summarizationsoftware developmentsoftware maintenancedeep learning |
| spellingShingle | Sarah Aljumah Lamia Berriche Bi-LSTM-Based Neural Source Code Summarization Applied Sciences software engineering neural network code summarization software development software maintenance deep learning |
| title | Bi-LSTM-Based Neural Source Code Summarization |
| title_full | Bi-LSTM-Based Neural Source Code Summarization |
| title_fullStr | Bi-LSTM-Based Neural Source Code Summarization |
| title_full_unstemmed | Bi-LSTM-Based Neural Source Code Summarization |
| title_short | Bi-LSTM-Based Neural Source Code Summarization |
| title_sort | bi lstm based neural source code summarization |
| topic | software engineering neural network code summarization software development software maintenance deep learning |
| url | https://www.mdpi.com/2076-3417/12/24/12587 |
| work_keys_str_mv | AT sarahaljumah bilstmbasedneuralsourcecodesummarization AT lamiaberriche bilstmbasedneuralsourcecodesummarization |