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Usage

The tool has two primary arguments:

  • --arxiv: This is the Arxiv URL to download and evaluate.
  • --repo: This is the Git repository to evaluate.

You can also set the logging level using the --log-level argument.

Examples

# Paper 2111.12673 from the gold standard dataset 
reproscreener --arxiv https://arxiv.org/e-print/2111.12673 --repo https://github.com/nicolinho/acc

# Paper 2106.07704 from the gold standard dataset
reproscreener --arxiv https://arxiv.org/e-print/2106.07704 --repo https://github.com/HanGuo97/soft-Q-learning-for-text-generation

# Paper 2203.06735 from the gold standard dataset
reproscreener --arxiv https://arxiv.org/e-print/2203.06735 --repo https://github.com/ghafeleb/Private-NonConvex-Federated-Learning-Without-a-Trusted-Server

# Run the tool with logging level set to debug
reproscreener --arxiv https://arxiv.org/e-print/2111.12673 --repo https://github.com/nicolinho/acc --log-level debug

By default, the logging level is set to warning. This means that only warnings, errors, and critical issues will be logged.

If you want to see more detailed logs, you can set the logging level to debug.

Project structure

  • case-studies contains the papers that reproscreener is developed and tested on
  • guidance contains the set of metrics that reproscreener will check for
  • tests contains the unit tests for reproscreener
  • src/reproscreener contains the main python scripts