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Automated testing: Motivation


Teaching: 10 min
Exercises: 0 min
  • Why should we write tests?
  • Discuss the advantages of writing and maintaining tests in research software.

Untested software can be compared to uncalibrated detectors

Before relying on a new experimental device, an experimental scientist always establishes its accuracy. A new detector is calibrated when the scientist observes its responses to known input signals. The results of this calibration are compared against the expected response.

Simulations and analysis using software should be held to the same standards!

Adapted from Testing and Continuous Integration with Python, created by Kathryn Huff

Further motivation for testing:

Testing in a nutshell

In software tests, expected results are compared with observed results in order to establish accuracy:

def fahrenheit_to_celsius(temp_f):
    Converts temperature in Fahrenheit
    to Celsius.
    temp_c = (temp_f - 32.0) * (5.0/9.0)
    return temp_c

def test_fahrenheit_to_celsius():
    temp_c = fahrenheit_to_celsius(temp_f=100.0)
    expected_result = 37.777777
    assert abs(temp_c - expected_result) < 1.0e-6

Why are we not comparing directly all digits with the expected result?

Tests make sure that expected functionality is preserved

  • As projects grow, it becomes easier to break things without noticing immediately
  • Testing helps detecting errors early
  • Interpreted dynamically typed imperative languages need to be tested
  • So do compiled languages but the compiler catches at least some errors
  • Testing is essential for research software because we care about reproducibility of scientific results and because derivative research and programs depend on research software
  • “Program testing can be used to show the presence of bugs, but never to show their absence!” (Edsger W. Dijkstra)

Tests help users of your code

  • Make it easier for others to verify whether the code has been correctly installed
  • Users of your code publish papers based on results produced by your code
  • Your peers need to be able to reproduce your (to be) published computational results

Tests help developers of your code

  • Tests make it possible to refactor the code
  • Code is unsustainable without runnable tests and becomes legacy software
  • Documentation which is up to date by definition
  • Easier for external developers to contribute to the project without breaking your code

Suiting up to modify untested code:

Tests help managing complexity

  • Well structured code is easy to test
  • Badly structured code is difficult to test automatically
  • Tests guide towards modular code structure

Good code: pure and easy to test

def fahrenheit_to_celsius(temp_f):
    temp_c = (temp_f - 32.0) * (5.0/9.0)
    return temp_c

temp_c = fahrenheit_to_celsius(temp_f=100.0)

Less good code: has side effects and is difficult to test

f_to_c_offset = 32.0
f_to_c_factor = 0.555555555
temp_c = 0.0

def fahrenheit_to_celsius_bad(temp_f):
    global temp_c
    temp_c = (temp_f - f_to_c_offset) * f_to_c_factor


Would you trust a code …

  • … when its tests do not pass?
  • … if there are no tests at all?
  • … if the tests are never run?

Key points

  • Tests make sure that expected functionality is preserved

  • Tests help both users and developers of your code

  • Tests help managing complexity