Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (5.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: matifkhattak
- License: mit
- Language: Python
- Default Branch: main
- Size: 1.14 GB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 2
Metadata Files
README.md
Abstract
Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains, i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity, etc. Metamorphic Testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., CNNs) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this paper, we attempt to address this problem by using a Statistical Metamorphic Testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the Metamorphic Relations (MRs).
Citation
If you use this repository in your research, please cite it. See CITATION for details.
Owner
- Name: Faqeer ur Rehman
- Login: matifkhattak
- Kind: user
- Repositories: 1
- Profile: https://github.com/matifkhattak
I am a Full Stack .Net developer having more than 7 years of professional experience in designing and developing large scale ERP solutions.
GitHub Events
Total
- Release event: 1
- Push event: 5
- Create event: 1
Last Year
- Release event: 1
- Push event: 5
- Create event: 1