Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (5.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

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
Created almost 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

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

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

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Last Year
  • Release event: 1
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