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Course Library · Lesson 2 of 6
Digital · Guided lesson

Capture equivalence across the input space

About 22 minutes · 4 reading + 18 practice and checks
Time includes reading, practice, and questions. Extra videos or further study are optional.
See this course’s project requirements

A before/after fictional fee calculator split into pure functions with an executable equivalence suite, clearer names and documented intentional nonchanges.

Keep your working files in your own notebook or software. Save a private description, checklist, and work link in My Proof.

What to include

  • Produce a complete valid/rejected behavior contract and intentional-change boundary.
  • Retain full finite valid-space coverage and explicitly scoped invalid-case comparisons.
  • Create checked validation/subtotal helpers and preserve the public entry contract.
  • Produce a domain-name/reference review with unchanged discount and delivery semantics.
  • Retain a passing baseline and an expected failing price mutation without altering final pricing.
  • Deliver an independently reasoned, repeatable refactor with finite coverage and selected-invalid limits.

Review and revise

Use these criteria to check your work and choose what to improve.

  • The implementation or declared simulation follows the supplied contract and retains inspectable original inputs.
  • Normal, boundary and rejected cases are recorded with expected versus observed results.
  • The changed case has a correct explanation and does not rely on hidden state.
  • The project includes source, outputs, instructions and honest tool, privacy and verification limits.
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Before you begin

The idea in everyday life

Fictional workshop kits: quantity 1–20, $3 per kit, a 20% member discount on kit subtotal, and $2.50 only for post. Calculations return integer cents. legacy_fee.py and refactored_fee.py must agree on permitted and rejected requests.

The idea

Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

Follow along

Capture equivalence across the input space — original worked practice

Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

Fictional workshop kits: quantity 1–20, $3 per kit, a 20% member discount on kit subtotal, and $2.50 only for post. Calculations return integer cents. legacy_fee.py and refactored_fee.py must agree on permitted and rejected requests.

  1. 1. Step 1

    Read test-equivalence.py iterating 20 quantities, two member flags, and two delivery modes.

    You should see: There are 80 valid combinations.

    Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

  2. 2. Step 2

    Inspect result(fn,args), which records either a value or ValueError type/message.

    You should see: Successful and rejected behavior are compared in a consistent form.

    Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

  3. 3. Step 3

    Run python3 test-equivalence.py before making further edits.

    You should see: 88 refactor equivalence cases passed.

    Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

  4. 4. Step 4

    Retain the 80 valid plus 8 invalid coverage description and actual output.

    You should see: A baseline equivalence-suite record.

    Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination.

Now change one thing

Quantity range changes to 1–10 with the same two flags and two modes. How many valid combinations are there?

Your work

What to finish

Retain full finite valid-space coverage and explicitly scoped invalid-case comparisons.

Common mistake

Watch for this

Claiming selected invalid examples exhaust every possible malformed Python object.

How can I tell I understand it?

Demonstrate the rule: Characterization tests record current behavior across meaningful cases. Here the valid input space is small enough to enumerate every declared combination. Use the supplied variant to distinguish a changed result from a broken implementation.

Try 3 questions

Quick knowledge check

Answer all 3 questions. Get at least 2 right and the lesson completes automatically.

Not yet passed

You’ll get an explanation for each answer. Signed in? Your results save to your account.

1How many valid kit combinations does the supplied loop compare?
2Why compare exception type and message here?
3What does enumeration cover in this course?
0 of 3 answeredApply the lesson to a short question, then review the explanation.
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Finish this lesson

Pass the check. We’ll save the progress.

Answer all 3 questions above and get at least 2 right. You can review the explanations and try again.

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Sources and further reading

Explore the references behind this lesson.

docs.python.org — unittest.html
Source & access notes · checked 2026-10-01

Relevant documented module/API behavior inspected during expansion authoring; actual execution evidence is recorded separately. No grant eligibility is established.

Access: Official page retrieved with the web reader on 2026-10-01. Documentation reading does not certify execution in every native tool or browser.

Reuse: Original local practice files, fictional data, worked examples and questions. Concepts paraphrased and linked; source exercises are not copied.

docs.python.org — controlflow.html
Source & access notes · checked 2026-10-01

Relevant documented module/API behavior inspected during expansion authoring; actual execution evidence is recorded separately. No grant eligibility is established.

Access: Official page retrieved with the web reader on 2026-10-01. Documentation reading does not certify execution in every native tool or browser.

Reuse: Original local practice files, fictional data, worked examples and questions. Concepts paraphrased and linked; source exercises are not copied.

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