رکورد قبلیرکورد بعدی

" Learning Python testing : "


Document Type : BL
Record Number : 855819
Main Entry : Arbuckle, Daniel
Title & Author : Learning Python testing : : a straightforward and easy approach to testing your Python projects /\ Daniel Arbuckle.
Edition Statement : Second edition.
Publication Statement : Birmingham, UK :: Packt Publishing,, 2014.
Series Statement : Community experience distilled
Page. NO : 1 online resource (1 volume) :: illustrations
ISBN : 1783553219
: : 1783553227
: : 9781783553211
: : 9781783553228
: 9781783553211
Notes : Includes index.
Contents : Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Python and Testing; Testing for fun and profit; Levels of testing; Unit testing; Integration testing; System testing; Acceptance testing; Regression testing; Test-driven development; You'll need Python; Summary; Chapter 2: Working with doctest; Where doctest performs best; The doctest language; Example -- creating and running a simple doctest; Result -- three times three does not equal ten; The syntax of doctests; Example -- a more complex test; Result -- five tests run?
: Example -- a doctest in a docstringResult -- the code is now self-documenting and self-testable; Putting it into practice -- an AVL tree; English specification; Node data; Testing the constructor; Recalculating height; Making a node deletable; Rotation; Locating a node; The rest of the specification; Summary; Chapter 3: Unit Testing with doctest; What is unit testing?; The limitations of unit testing; Example -- identifying units; Choosing units; Check your understanding; Unit testing during the development process; Design; Development; Feedback; Development, again; Later stages of the process.
: Expecting exceptionsExample -- checking for an exception; Result -- success at failing; Expecting blank lines; Controlling doctest behavior with directives; Ignoring part of the result; Example -- ellipsis test drive; Result -- ellipsis elides; Ignoring white space; Example -- invoking normality; Result -- white space matches any other white space; Skipping an example; Example -- humans only; Result -- it looks like a test, but it's not; The other directives; The execution scope of doctest tests; Check your understanding; Exercise -- English to doctest; Embedding doctests into docstrings.
: The basicsAssertions; The assertTrue method; The assertFalse method; The assertEqual method; The assertNotEqual method; The assertAlmostEqual method; The assertNotAlmostEqual method; The assertIs and assertIsNot methods; The assertIsNone and assertIsNotNone methods; The assertIn and assertNotIn methods; The assertIsInstance and assertNotIsInstance methods; The assertRaises method; The fail method; Make sure you get it; Test fixtures; Example -- testing database-backed units; Summary; Chapter 6: Running Your Tests with Nose; Installing Nose; Organizing tests; An example of organizing tests.
Abstract : Chapter 4: Decoupling Units with unittest.mock; Mock objects in general; Mock objects according to unittest.mock; Standard mock objects; Non-mock attributes; Non-mock return values and raising exceptions; Mocking class or function details; Mocking function or method side effects; Mocking containers and objects with a special behavior; Mock objects for properties and descriptors; Mocking file objects; Replacing real code with mock objects; Mock objects in action; Better PID tests; Patching time.time; Decoupling from the constructor; Summary; Chapter 5: Structured Testing with unittest.
: This book is ideal if you want to learn about the testing disciplines and automated testing tools from a hands-on, conversational guide. You should already know Python and be comfortable with Python 3.
Subject : Object-oriented programming (Computer science)
Subject : Python (Computer program language)
Subject : COMPUTERS-- Computer Literacy.
Subject : COMPUTERS-- Computer Science.
Subject : COMPUTERS-- Data Processing.
Subject : COMPUTERS-- Hardware-- General.
Subject : COMPUTERS-- Information Technology.
Subject : COMPUTERS-- Machine Theory.
Subject : COMPUTERS-- Reference.
Subject : Object-oriented programming (Computer science)
Subject : Python (Computer program language)
Dewey Classification : ‭004.6186‬
LC Classification : ‭QA76.73.P98‬
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