#python#arxiv#automation#e_mail#github_action#paper#recommendation#research#zotero
You can get daily emails recommending new arXiv research papers that match your interests by linking your Zotero library with the Zotero-arXiv-Daily tool. It automatically finds relevant papers based on what you have saved in Zotero, summarizes them with AI-generated short descriptions, and sends you links to PDFs and code if available. This service is free, easy to set up on GitHub with minimal configuration, and runs automatically every day, saving you time and effort in keeping up with new scientific papers tailored to your research areas. It helps you stay updated without manually searching arXiv[1].
https://github.com/TideDra/zotero-arxiv-daily
https://code.tutsplus.com/tutorials/behavior-driven-development-in-python--net-26547
Behavior-Driven Development (which we will now refer to as "#BDD") follows on from the ideas and principles introduced in #Test-Driven Development. The key points of writing tests before code really apply to BDD as well. The idea is to not only test your code at the granular level with unit tests, but also test your application end to end, using acceptance tests. We will introduce this style of testing with the use of the Lettuce testing framework.
http://pythonhosted.org/behave/
behave is behaviour-driven development, Python style.
Behavior-driven development (or #BDD) is an agile software development technique that encourages collaboration between developers, #QA and non-technical or business participants in a software project. We have a page further describing this philosophy.
behave uses tests written in a natural language style, backed up by Python code.
Once you’ve installed behave, we recommend reading the
tutorial first and then
feature test setup,
behave API and
related software (things that you can combine with behave)
finally: how to use and configure the behave tool.
https://pypi.python.org/pypi/pytest-bdd
#BDD library for the py.test runner
#pytest-bdd implements a subset of Gherkin language for the automation of the project requirements testing and easier behavioral driven development.
Unlike many other BDD tools it doesn’t require a separate runner and benefits from the power and flexibility of the #pytest. It allows to unify your unit and functional #tests, easier continuous integration server configuration and maximal reuse of the tests setup.
Pytest fixtures written for the #unit_test s can be reused for the setup and actions mentioned in the feature steps with dependency injection, which allows a true BDD just-enough specification of the requirements without maintaining any context object containing the side effects of the Gherkin. imperative declarations.