Identifying metamorphic relations: A data mutation directed approach Article Swipe
Chang‐ai Sun
,
Hui Jin
,
SiYi Wu
,
An Fu
,
ZuoYi Wang
,
W. K. Chan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/spe.3280
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/spe.3280
Summary Metamorphic testing (MT) is an effective technique to alleviate the test oracle problem. The principle of MT is to detect failures by checking whether some necessary properties, commonly known as metamorphic relations (MRs), of software under test (SUT) hold among multiple executions of source and follow‐up test cases. Since both the generation of follow‐up test cases and test result verification depend on MRs, the identification of MRs plays a key role in MT, which is an important yet difficult task requiring deep domain knowledge of the SUT. Accordingly, techniques that can direct a tester to identify MRs effectively are desirable. In this paper, we propose MT, a data mutation directed approach to identifying MRs. MT guides a tester to identify MRs by providing a set of data mutation operators and template‐style mapping rules, which not only alleviates the difficulties faced in the process of MR identification but also improves the identification effectiveness. We have further developed a tool to implement the proposed approach and conducted an empirical study to evaluate the MR identification effectiveness of MT and the performance of MRs identified by MT with respect to fault detection capability and statement coverage. The empirical results show that MT is able to identify MRs for numeric programs effectively, and the identified MRs have high fault detection capability and statement coverage. The work presented in this paper advances the field of MT by providing a simple yet practical approach to the MR identification problem.
Related Topics
Concepts
Computer science
Identification (biology)
Oracle
Statement (logic)
Artificial intelligence
Process (computing)
Machine learning
Data mining
Task (project management)
Set (abstract data type)
Test data
Domain (mathematical analysis)
Fault (geology)
Field (mathematics)
Programming language
Engineering
Systems engineering
Seismology
Geology
Botany
Biology
Mathematics
Political science
Mathematical analysis
Law
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/spe.3280
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/spe.3280
- OA Status
- bronze
- Cited By
- 2
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387739662
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387739662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/spe.3280Digital Object Identifier
- Title
-
Identifying metamorphic relations: A data mutation directed approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-18Full publication date if available
- Authors
-
Chang‐ai Sun, Hui Jin, SiYi Wu, An Fu, ZuoYi Wang, W. K. ChanList of authors in order
- Landing page
-
https://doi.org/10.1002/spe.3280Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/spe.3280Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/spe.3280Direct OA link when available
- Concepts
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Computer science, Identification (biology), Oracle, Statement (logic), Artificial intelligence, Process (computing), Machine learning, Data mining, Task (project management), Set (abstract data type), Test data, Domain (mathematical analysis), Fault (geology), Field (mathematics), Programming language, Engineering, Systems engineering, Seismology, Geology, Botany, Biology, Mathematics, Political science, Mathematical analysis, Law, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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