Please use this identifier to cite or link to this item: https://elib.vku.udn.vn/handle/123456789/4058
Title: A comparative study of deep learning techniques in software fault prediction
Authors: Ha, Thi Minh Phuong
Nguyen, Thi Kim Ngan
Nguyen, Thanh Binh
Keywords: Software engineering
deep learning
software fault prediction
abstract syntax tree
software faults
Issue Date: Jun-2024
Publisher: The University of Da Nang, Journal of Science and Technology
Abstract: 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.
Description: The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05.
URI: https://jst-ud.vn/jst-ud/article/view/9255/6222
https://elib.vku.udn.vn/handle/123456789/4058
ISSN: 1859-1531
Appears in Collections:NĂM 2024

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