https://github.com/cthoyt/autoreviewer
👁️🗨️ Scientists often do the same bad stuff. Automate giving feedback during peer review.
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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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary
Keywords
Repository
👁️🗨️ Scientists often do the same bad stuff. Automate giving feedback during peer review.
Basic Info
Statistics
- Stars: 26
- Watchers: 2
- Forks: 0
- Open Issues: 6
- Releases: 4
Topics
Metadata Files
README.md
AutoReviewer
Scientists often do the same bad stuff. Automate giving feedback during peer review for Python packages.
Goals:
- Given a GitHub repository, automate finding common issues such as
- No setup.py/setup.cfg/pyproject.toml
- No zenodo archive linked from the README
- Non-standard code layout (
src/or bust) - Files contain hard-coded file paths
- No documentation (search README for link to readthedocs)
- Package name doesn't match github repository name
- No reproducible installation instructions (i.e., does the README contain
pip) - Uses conda for installation
- Code does not have consistent style (i.e., there's no configuration for
blackorruff) pyromadoesn't pass 10/10- missing
LICENSEfile - missing
CITATION.cfffile
- Automate sending issues to the repository instructing how to do these things
- Use deterministic titles for all issues to avoid duplicates / make idempotent
- Create and edit "epic" issue that links others
Example Reviews:
- https://github.com/fanavarro/lexical-analysis-obo-foundry/issues/4
- https://github.com/krishnanlab/PecanPy/issues/12
- https://github.com/huihui1126/drugSim-pathway/issues/14
Want to collaborate? What do you expect out of Python packages? Let me know in the comments. I envision this being sort of modular so people can contribute their own checks.
Desired interface:
Run on the command line with:
shell
$ autoreviewer https://github.com/rs-costa/sbml2hyb
Cross-venue Analysis
There's a submodule autoreviewer.comparison that has utilities for scraping
the paper list from the Journal of Cheminformatics and several other journals,
getting their ePub files, extracting GitHub references from the availability
statements, running autoreview on each, then making this summary.
There is an important caveat with respect to GitHub repository identification.
BMC Bioinformatics and the Journal of Cheminformatics both have a standardized section for referencing code. JOSS and JMLR-MLOSS both have dedicated metadata slots for repository references. This leaves JMLR, NeurIPS, ICLR, and any other resource where GitHub repository links are extracted from PDF as having lots of false positives, since authors typically reference other source code via GitHub link rather than citation.
🚀 Installation
The most recent release can be installed from PyPI with:
bash
$ pip install autoreviewer
The most recent code and data can be installed directly from GitHub with:
bash
$ pip install git+https://github.com/cthoyt/autoreviewer.git
You'll also need to make sure pandoc is installed. The
best way to do this is brew install pandoc on macOS.
👐 Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.
👋 Attribution
⚖️ License
The code in this package is licensed under the MIT License.
🍪 Cookiecutter
This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.
🛠️ For Developers
See developer instructions
The final section of the README is for if you want to get involved by making a code contribution. ### Development Installation To install in development mode, use the following: ```bash $ git clone git+https://github.com/cthoyt/autoreviewer.git $ cd autoreviewer $ pip install -e . ``` ### 🥼 Testing After cloning the repository and installing `tox` with `pip install tox`, the unit tests in the `tests/` folder can be run reproducibly with: ```shell $ tox ``` Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/cthoyt/autoreviewer/actions?query=workflow%3ATests). ### 📖 Building the Documentation The documentation can be built locally using the following: ```shell $ git clone git+https://github.com/cthoyt/autoreviewer.git $ cd autoreviewer $ tox -e docs $ open docs/build/html/index.html ``` The documentation automatically installs the package as well as the `docs` extra specified in the [`setup.cfg`](setup.cfg). `sphinx` plugins like `texext` can be added there. Additionally, they need to be added to the `extensions` list in [`docs/source/conf.py`](docs/source/conf.py). ### 📦 Making a Release After installing the package in development mode and installing `tox` with `pip install tox`, the commands for making a new release are contained within the `finish` environment in `tox.ini`. Run the following from the shell: ```shell $ tox -e finish ``` This script does the following: 1. Uses [Bump2Version](https://github.com/c4urself/bump2version) to switch the version number in the `setup.cfg`, `src/autoreviewer/version.py`, and [`docs/source/conf.py`](docs/source/conf.py) to not have the `-dev` suffix 2. Packages the code in both a tar archive and a wheel using [`build`](https://github.com/pypa/build) 3. Uploads to PyPI using [`twine`](https://github.com/pypa/twine). Be sure to have a `.pypirc` file configured to avoid the need for manual input at this step 4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped. 5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use `tox -e bumpversion minor` after.Owner
- Name: Charles Tapley Hoyt
- Login: cthoyt
- Kind: user
- Location: Bonn, Germany
- Company: RWTH Aachen University
- Website: https://cthoyt.com
- Repositories: 489
- Profile: https://github.com/cthoyt
GitHub Events
Total
- Watch event: 5
- Delete event: 1
- Push event: 8
- Pull request event: 2
Last Year
- Watch event: 5
- Delete event: 1
- Push event: 8
- Pull request event: 2
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 8
- Total pull requests: 4
- Average time to close issues: 1 day
- Average time to close pull requests: about 7 hours
- Total issue authors: 5
- Total pull request authors: 1
- Average comments per issue: 0.63
- Average comments per pull request: 0.75
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 1
- Average time to close issues: 2 days
- Average time to close pull requests: about 11 hours
- Issue authors: 3
- Pull request authors: 1
- Average comments per issue: 1.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- cthoyt (4)
- kjappelbaum (1)
- vishaldeyiiest (1)
- YojanaGadiya (1)
- MiJia-ID (1)
Pull Request Authors
- cthoyt (6)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 41 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 5
- Total maintainers: 1
pypi.org: autoreviewer
Automate scientific software review
- Homepage: https://github.com/cthoyt/autoreviewer
- Documentation: https://autoreviewer.readthedocs.io/
- License: MIT
-
Latest release: 0.0.5
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- codecov/codecov-action v1 composite