This is a Hackday project at the SSI Collaborations Workshop 2020.
This is a Python-based command-line tool for parsing Gherkin code in Markdown cells in a Jupyter Notebook and generating skeleton unit tests which are then inserted into the Notebook at the appropriate points.
The idea of this project is improve the Jupyter notebooks by adding validations but all this is not something new, there is already something available as a plug in for Jupyter called nbval which work nice if you want to use the TDD approach which sometime can be a little bit painful when you are in an early stage prototyping you research. Since TDD (Test Driven Development) can be a little be to much from the beginning we found that it may be helpful to start with a BDD (Behavior-driven development) approach by writing test cases in a natural language that non-programmers can read and also serve as a documentation more or less.
- Create initial notebook
- Start writing test cases using natural language combined with Gherkin behaviour markup
- nbbdd generates feature and skeleton step files, inserts step code, returns failing test results
- Populate skeleton tests with real test code
- Start write software while keeping in mind running also the tests until all pass
Some simple example for step 2 using behave
Feature: Fight or Flight (Natural Language, tutorial02)
In order to increase the ninja survival rate,
As a ninja commander
I want my ninjas to decide whether to take on an opponent
based on their skill levels.
Scenario: Weaker opponent
Given the ninja has a third level black-belt
When attacked by a samurai
Then the ninja should engage the opponent
Scenario: Stronger opponent
Given the ninja has a third level black-belt
When attacked by Chuck Norris
Then the ninja should run for his life
Steps 4 and 5 can be identified as the TDD part which already requires some coding skills.
This paper helped a lot to understand what should be achieved during the hackday:
Fangohr, H. & Fauske, Vidar & Kluyver, Thomas & Albert, Maximilian & Laslett, Oliver & Cortés-Ortuño, David & Beg, Marijan & Ragan-Kelly, Min. (2020). Testing with Jupyter notebooks: NoteBook VALidation (nbval) plug-in for pytest.