MetBench: A Numerical Expression Metamorphic Relations Benchmark Data Set

Metamorphic testing is one of the most effective methods to alleviate the oracle problem. However, the lack of benchmark data sets has hindered the development of metamorphic testing. This study identifies and extracts key characteristics from three entities: the business domain, applications, and metamorphic relations. It then establishes a model for numerical expression metamorphic relations. Data were gathered from open source programs, academic papers, and industrial software, which resulted in the construction of the MetBench benchmark data set encompassing 139 codes and 924 metamorphic relations. In addition, we developed a general purpose tool for automating the execution of metamorphic testing, which separates code, metamorphic relations, and testing processes. This tool supports the management of metamorphic relations, the automated execution of metamorphic testing, and the visualization of the results. Specifically, metamorphic relations are encoded in the literal format of LaTeX and the computational format of SymPy and are stored using the document-oriented database LiteDB. The system utilizes a combination of C# and Python programming languages. The former is responsible for the user interface, while the latter handles the automated execution. Visualization is achieved through Matplotlib. The establishment of the MetBench benchmark data set facilitates the study of metamorphic relation identification techniques, the evaluation of the effectiveness of relations, and their nature characteristics such as complexity, similarity, and false satisfaction rate.

Authors

Institutions

Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/dsa63982.2024.00026
Citations
1
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.78

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MetBench: A Numerical Expression Metamorphic Relations Benchmark Data Set

Yung-Ting Su, Meng Li, Yin Zhao, Cong Zeng et al.
1 citations
Data Mining Algorithms and Applications
0.78
article

MetBench: A Numerical Expression Metamorphic Relations Benchmark Data Set

Yung-Ting Su, Meng Li, Yin Zhao, Cong Zeng, Kailin Zi
article en
1 citations

Abstract

Metamorphic testing is one of the most effective methods to alleviate the oracle problem. However, the lack of benchmark data sets has hindered the development of metamorphic testing. This study identifies and extracts key characteristics from three entities: the business domain, applications, and metamorphic relations. It then establishes a model for numerical expression metamorphic relations. Data were gathered from open source programs, academic papers, and industrial software, which resulted in the construction of the MetBench benchmark data set encompassing 139 codes and 924 metamorphic relations. In addition, we developed a general purpose tool for automating the execution of metamorphic testing, which separates code, metamorphic relations, and testing processes. This tool supports the management of metamorphic relations, the automated execution of metamorphic testing, and the visualization of the results. Specifically, metamorphic relations are encoded in the literal format of LaTeX and the computational format of SymPy and are stored using the document-oriented database LiteDB. The system utilizes a combination of C# and Python programming languages. The former is responsible for the user interface, while the latter handles the automated execution. Visualization is achieved through Matplotlib. The establishment of the MetBench benchmark data set facilitates the study of metamorphic relation identification techniques, the evaluation of the effectiveness of relations, and their nature characteristics such as complexity, similarity, and false satisfaction rate.

University of South China (CN)
University of South China
Openalex Percentile: Top 18%
Data Mining Algorithms and Applications
0.78
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.