Functional testing

As explained in Practices of the Python Pro

A functional test evaluates software behavior by supplying input, determining expected output, obtaining actual output, and comparing the results.

Functional testing checks whether software produces the expected output for a given input. A test follows a simple recipe: prepare inputs, identify the expected output, obtain the actual output, and compare the two. Because this structure describes how code should behave, a good test can also serve as practical documentation. section 5.1.2

Apply the recipe at different scales:

• For a function, give calculate_mean the list [1, 2, 3, 4] and expect 2.5. section 5.1.2
• For an application workflow, add product 53-DE-232 to a cart and expect the product to appear there. section 5.1.2
• Use unit and integration tests liberally, while treating long, brittle, or manual tests more conservatively. section 5.2.6

Testing a product transformation

With Python's unittest, create Product('shoes', 'S', 'black'), set the expected value to 'SHOES', call transform_name_for_sku(), and compare the result with assertEqual(expected_value, actual_value). A failing assertion reports the differing values, helping locate the defect. Extend the same pattern to color transformation, SKU generation, cart state, and edge cases such as removing more products than the cart contains. section 5.4.3 section 5.4.6

Figure 5.1. The basic flow of a functional test

Use the flow diagram to scan the relationship between inputs, expected output, actual output, and comparison. section 5.1.2

How Practices of the Python Pro covers it

How deep it goes

Defines four test steps, demonstrates Python implementation, and compares unit, integration, manual, and performance testing without low-level internals.

Examples

Uses mean calculation, e-commerce cart behavior, product SKU transformations, shopping-cart assertions, and an over-removal edge case.

What it assumes

Assumes enough Python to create objects, call methods, and run a test command, while teaching the functional-test recipe explicitly.

What you can do after

Apply it via unittest or pytest to write assertions, detect failures, test alternate outcomes, and build confidence incrementally.

How it connects

Connects functional testing with unit testing, integration testing, manual testing, performance testing, and test-driven development.

liveBookmanning.com

Read Practices of the Python Pro on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Functional Design and Architecture: Examples in HaskellTesting that evaluates specified functionality or an isolated unit of application logic without invoking real external services.

Compare how 2 books explain functional testing →

Related concepts

• testkind of · A test is a procedure or check that evaluates behavior using specified inputs or evidence against expected results or criteria.
• performance testingcontrasts with · Performance testing is the evaluation of a system's behavior, such as its responsiveness, throughput, scalability, stability, or resource use, under specified workloads and operating conditions.
• labeluses · A label is a value or annotation associated with an item that identifies its target category, outcome, or reference answer for learning or evaluation.
• Seleniumimplemented by · Selenium is an open-source suite of tools and APIs for automating web browsers and testing web applications.
• generationuses · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Practices of the Python Pro. The section links point to the parts of the book it draws on.