A Rich High-Order Mutation Testing Dataset for Software Fortification

A Rich High-Order Mutation Testing Dataset for Software Fortification

Journal on Information Technologies & Communications; Vol 2025 No 1; pp: 19-27

Đã lưu trong:
书目详细资料
Những tác giả chính: Do, Van Nho, Tran, Giang T.C, Nguyen, Duc Thuan, Nguyen, Thi Ngoc Anh, Nguyen, Quang Vu, Nguyen, Thanh Binh
格式: Bài viết
语言:English
出版: Journal on Information Technologies & Communications 2025
主题:
High order mutation testing
Data
dataset
data generation
machine learning
在线阅读:https://doi.org/10.32913/mic-ict-research.v2024.n2.1277
https://elib.vku.udn.vn/handle/123456789/5787
标签: 添加标签
没有标签, 成为第一个标记此记录!
Thư viện lưu trữ: Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng
id oai:elib.vku.udn.vn:123456789-5787
record_format dspace
spelling oai:elib.vku.udn.vn:123456789-57872025-11-11T04:28:49Z A Rich High-Order Mutation Testing Dataset for Software Fortification Do, Van Nho Tran, Giang T.C Nguyen, Duc Thuan Nguyen, Thi Ngoc Anh Nguyen, Quang Vu Nguyen, Thanh Binh High order mutation testing Data dataset data generation machine learning Journal on Information Technologies & Communications; Vol 2025 No 1; pp: 19-27 High-order mutation (HOM) testing is a rigorous technique for evaluating the effectiveness of test suites by introducing mutations with multiple concurrent faults into the source code. In this study, we present the development and analysis of a comprehensive dataset tailored for HOM testing purposes. The dataset comprises 2,839,792 instances categorized into Survived and Killed classes, representing instances correctly identified as surviving and not surviving the mutation testing process, respectively. We employ four prominent machine learning algorithms—Logistic Regression, Random Forest Classifier, LightGBM, and XGBoost—to classify instances within these categories. Experimental results demonstrate varying levels of accuracy, precision, recall, and F1-score across the algorithms, with LightGBM and XGBoost exhibiting superior performance. These findings underscore the importance of high-quality datasets in facilitating effective HOM testing and provide valuable insights into the capabilities of machine learning algorithms in this context. 2025-11-11T04:28:34Z 2025-11-11T04:28:34Z 2024-12 Working Paper 1859-3534 https://doi.org/10.32913/mic-ict-research.v2024.n2.1277 https://elib.vku.udn.vn/handle/123456789/5787 en application/pdf Journal on Information Technologies & Communications
institution Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng
collection DSpace
language English
topic High order mutation testing
Data
dataset
data generation
machine learning
spellingShingle High order mutation testing
Data
dataset
data generation
machine learning
Do, Van Nho
Tran, Giang T.C
Nguyen, Duc Thuan
Nguyen, Thi Ngoc Anh
Nguyen, Quang Vu
Nguyen, Thanh Binh
A Rich High-Order Mutation Testing Dataset for Software Fortification
description Journal on Information Technologies & Communications; Vol 2025 No 1; pp: 19-27
format Working Paper
author Do, Van Nho
Tran, Giang T.C
Nguyen, Duc Thuan
Nguyen, Thi Ngoc Anh
Nguyen, Quang Vu
Nguyen, Thanh Binh
author_facet Do, Van Nho
Tran, Giang T.C
Nguyen, Duc Thuan
Nguyen, Thi Ngoc Anh
Nguyen, Quang Vu
Nguyen, Thanh Binh
author_sort Do, Van Nho
title A Rich High-Order Mutation Testing Dataset for Software Fortification
title_short A Rich High-Order Mutation Testing Dataset for Software Fortification
title_full A Rich High-Order Mutation Testing Dataset for Software Fortification
title_fullStr A Rich High-Order Mutation Testing Dataset for Software Fortification
title_full_unstemmed A Rich High-Order Mutation Testing Dataset for Software Fortification
title_sort rich high-order mutation testing dataset for software fortification
publisher Journal on Information Technologies & Communications
publishDate 2025
url https://doi.org/10.32913/mic-ict-research.v2024.n2.1277
https://elib.vku.udn.vn/handle/123456789/5787
_version_ 1849386666521788416

相似书籍