A comparative study of deep learning techniques in software fault prediction

QR Kodea

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.

Gorde:

Xehetasun bibliografikoak
Egile Nagusiak:	Ha, Thi Minh Phuong, Nguyen, Thi Kim Ngan, Nguyen, Thanh Binh
Formatua:	Bài viết
Hizkuntza:	English
Argitaratua:	The University of Da Nang, Journal of Science and Technology 2024
Gaiak:	Software engineering deep learning software fault prediction abstract syntax tree software faults
Sarrera elektronikoa:	https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Thư viện lưu trữ:	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng

Antzeko izenburuak

Performance Analysis of Deep Learning Models for Software Fault Prediction Using the BugHunter Dataset
nork: Dang, Thi Kim Ngan, et al.
Argitaratua: (2025)

A Comparative Study of Wrapper Feature Selection Techniques in Software Fault Prediction
nork: Nguyen, Thanh Long, et al.
Argitaratua: (2023)

A combination of feature selection and data sampling techniques for software fault prediction
nork: Ha, Thi Minh Phuong, et al.
Argitaratua: (2024)

A Study of Filter-Based Feature Selection in Software Fault Prediction
nork: Ha, Thi Minh Phuong, et al.
Argitaratua: (2023)

A comparative study of handling imbalanced data using generative adversarial networks for machine learning based software fault prediction
nork: Ha, Thi Minh Phuong, et al.
Argitaratua: (2025)

Enhancing Software Fault Localization with Variational Autoencoder and Residual Neural Networks
nork: Cao, Thi Nham, et al.
Argitaratua: (2025)

Research and Build a Deep Tree – Based Model for Software Defct Prediction
nork: Le, Thi Thuy Vy
Argitaratua: (2023)

Sequential Bayesian prediction in the presence of changepoints and faults /

Next-Term Academic Success Prediction Using Deep Learning
nork: Pham, Vu Thu Nguyet, et al.
Argitaratua: (2022)

Building an Entrance Score Prediction System using Deep Learning
nork: Nguyen, Viet Du, et al.
Argitaratua: (2025)

An overview of risk quantification issues for digitalized nuclear power plants using a static fault tree /

A study on machine fault diagnosis using decision tree /
nork: Nguyen, Ngoc-Tu.

Xử lý dữ liệu không cân bằng trong bài toán dự đoán lỗi phần mềm
nork: Lê, Song Toàn, et al.
Argitaratua: (2021)

Double-circuit transmission lines fault location algorithm for single line-to-ground fault /
nork: Yang, Xia.

Advanced automotive fault diagnosis : automotive technology: vehicle maintenance and repair
nork: Denton, Tom
Argitaratua: (2021)

Software Fault Prediction
nork: Kumar, Sandeep, et al.
Argitaratua: (2020)

Low cost rotor fault detection system for inverter driven induction motor using currents signal /
nork: Kim, Namhun.

An Adaptive Cyber Deception Approach for Active Cyber-attack Defense Method based on Deep Reinforcement Learning
nork: Nguyen, Chi Toan, et al.
Argitaratua: (2024)

Predicting higher order mutation score based on machine learning
nork: Do, Van Nho, et al.
Argitaratua: (2024)

Fuzzy logic application in fault diagnosis of transformers using dissolved gases /
nork: Hooshmand, Rahmat-Allah.

Fault tolerant control for switched linear systems
nork: Dongsheng Du, et al.
Argitaratua: (2015)

Building an application to classify gender, age, and emotion using Deep Learning
nork: Le, Dac Khoa
Argitaratua: (2024)

Detection of Driver Sleepiness using Deep Learning
nork: Trinh, Quang Tin
Argitaratua: (2023)

A Machine Learning Approach for Predictive Insights into Tight Junction Protein Functions
nork: Nguyen, Thi Tuyen, et al.
Argitaratua: (2024)

Fault tolerance in data gathering wireless sensor networks /
nork: Zhang, Yanchun.

Automated Software Engineering: A Deep Learning-Based Approach
nork: Satapathy, Suresh Chandra, et al.
Argitaratua: (2020)

Two-terminal numerical algorithm for single-phase arcing fault detection and fault location estimation based on the spectral /

Learn deep learning methods on the problem of identifying points and student codes
nork: Tran, Binh Minh
Argitaratua: (2022)

Deep Learning for Computer Vision with Python (1st Edition)
nork: Starter, Bundle, et al.
Argitaratua: (2023)

Building an application for identifying Birds using Deep Learning model
nork: Nguyen, Si Thien
Argitaratua: (2024)

The edge-fault-tolerant bipancyclicity of the even K-ary N-cube /
nork: Fang Jywe-Fei,.

New Trends in Software Methodologies, Tools and Techniques
nork: Fujita, Hamido, et al.
Argitaratua: (2013)

Image colorization using Deep Learning
nork: Nguyen, Tai Cuong
Argitaratua: (2022)

Software engineering: A practitioner's approach /
nork: Pressman Roger S
Argitaratua: (2001)

Building an app to identify Bird species using Deep Learning models
nork: Nguyen, Thi Quynh Nhu
Argitaratua: (2024)

Fault current waveform analysis of a flux-lock type SFCL according to LC Resonance condition of third winding /
nork: Lim, Sung-Hun.

Bringing design to software.
Argitaratua: (1996)

Nghiên cứu các kỹ thuật khai phá dữ liệu cho dự đoán lỗi phần mềm
nork: Hà, Thị Minh Phương
Argitaratua: (2021)

The FRTU-based intelligent fault location determination strategy in ubiquitous based distribution systems /
nork: Ko, Yun-Seok.

Preserving the fault-containment of ring protocols executed on trees /
nork: Yamauchi, Yukiko.