A comparative study of deep learning techniques in software fault prediction
The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05.
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| 言語: | English |
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The University of Da Nang, Journal of Science and Technology
2024
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| オンライン･アクセス: | https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058 |
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oai:elib.vku.udn.vn:123456789-40582024-08-01T03:19:07Z A comparative study of deep learning techniques in software fault prediction Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh Software engineering deep learning software fault prediction abstract syntax tree software faults The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05. Software fault prediction (SFP) is an important approach in software engineering that ensures software quality and reliability. Prediction of software faults helps developers identify faulty components in software systems. Several studies focus on software metrics which are input into machine learning models to predict faulty components. However, such studies may not capture the semantic and structural information of software that is necessary for building fault prediction models with better performance. Therefore, this paper discusses the effectiveness of deep learning models including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) that are utilized to construct fault prediction models based on the contextual information. The experiment, which has been conducted on seven Apache datasets, with Precision, Recall, and F1-score are performance metrics. The comparison results show that LSTM and RNN are potential techniques for building highly accurate fault prediction models. 2024-08-01T03:19:04Z 2024-08-01T03:19:04Z 2024-06 Working Paper 1859-1531 https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058 en application/pdf The University of Da Nang, Journal of Science and Technology |
| institution |
Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng |
| collection |
DSpace |
| language |
English |
| topic |
Software engineering deep learning software fault prediction abstract syntax tree software faults |
| spellingShingle |
Software engineering deep learning software fault prediction abstract syntax tree software faults Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh A comparative study of deep learning techniques in software fault prediction |
| description |
The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05. |
| format |
Working Paper |
| author |
Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh |
| author_facet |
Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh |
| author_sort |
Ha, Thi Minh Phuong |
| title |
A comparative study of deep learning techniques in software fault prediction |
| title_short |
A comparative study of deep learning techniques in software fault prediction |
| title_full |
A comparative study of deep learning techniques in software fault prediction |
| title_fullStr |
A comparative study of deep learning techniques in software fault prediction |
| title_full_unstemmed |
A comparative study of deep learning techniques in software fault prediction |
| title_sort |
comparative study of deep learning techniques in software fault prediction |
| publisher |
The University of Da Nang, Journal of Science and Technology |
| publishDate |
2024 |
| url |
https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058 |
| _version_ |
1849198246031785984 |