Making your life easier with automated testing of Python is the sole aim of this book. Because it's a cookbook, you can take things at your own. homeranking.info is a great source of knowledge for software developers. Here we share with you the best software development. Python Testing Cookbook [Greg L. Turnquist] on homeranking.info *FREE* shipping on qualifying offers. This cookbook is written as a collection of code recipes.
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abbreviation used for Python Testing Cookbook) by using --no-site-packages. 3. Activate print to screen, encode it in HTML, or generate a PDF document. Making your life easier with automated testing of Python is the sole aim of this book. Because it's a cookbook, you can take things at your own pace, in. [Packt] - Python Testing Cookbook - [Turnquist] ().pdf. Pages · Python Graphics Cookbook - Python Programming. Pages··
Testing the edges How to do it Kali Linux. It also delivers advice about how to get the most from automated testing, which is as much an art as a science. Metrics aren't just for defending yourself to management Capturing a bug in an automated test How to do it You don't have anything in your cart right now.
Chapter 3: Creating Testable Documentation with doctest. Printing out all your documentation including a status report. Updating the project-level script to run this chapter's doctests. Chapter 4: Updating the project-level script to run this chapter's BDD tests. Chapter 5: Creating a project-level script to verify this chapter's acceptance tests.
Chapter 6: Integrating Automated Tests with Continuous Integration. Generating a continuous integration report for Jenkins using NoseXUnit. Configuring Jenkins to run Python tests when scheduled.
Generating a CI report for TeamCity using teamcity-nose. Configuring TeamCity to run Python tests when scheduled. Chapter 7: Measuring your Success with Test Coverage. Updating the project-level script to provide coverage reports. Chapter 8: Defining a subset of test cases using import statements. Recording and playing back live data as fast as possible. Chapter 9: If you aren't convinced on the value of testing, your team won't be either.
Pause to refactor when test suite takes too long to run. Instead of shooting for percent coverage, try to have a steady growth.
What You Will Learn Get started with the basics of writing automated unit tests and asserting results Use Nose to discover tests and build suites automatically Write Nose plugins that control what tests are discovered and how to produce test reports Add testable documentation to your code Filter out test noise, customize test reports, and tweak doctest's to meet your needs Write testable stories using lots of tools including doctest, mocks, Lettuce, and Should DSL Get started with the basics of customer-oriented acceptance testing Test the web security of your application Configure Jenkins and TeamCity to run your test suite upon check-in Capture test coverage reports in lots of formats, and integrate with Jenkins and Nose Take the pulse of your system with a quick smoke test and overload your system to find its breaking points Add automated testing to an existing legacy system that isn't test oriented.
Authors Greg L. Read More. Read More Reviews.
Every Packt product delivers a specific learning pathway, broadly defined by the Series type. This structured approach enables you to select the pathway which best suits your knowledge level, learning style and task objectives.
As a new user, these step-by-step tutorial guides will give you all the practical skills necessary to become competent and efficient. Beginner's Guide. Friendly, informal tutorials that provide a practical introduction using examples, activities, and challenges. Fast paced, concentrated introductions showing the quickest way to put the tool to work in the real world.
There's more The plugin isn't installable See also Testing separate doctest documents Getting ready How to do it Doesn't this defy the usability of docstrings? Writing a testable story with doctest Getting ready How to do it Writing a testable novel with doctest Getting ready How to do it Writing a testable story with Voidspace Mock and nose Getting ready How to do it Tell me more about the spec nose plugin!
Why didn't we reuse the plugin from the recipe 'Naming tests so they sound like sentences and stories'? See also Writing a testable story with mockito and nose Getting ready How to do it See also Writing a testable story with Lettuce Getting ready How to do it How complex should a story be?
See also 5. See also Testing the basics with Pyccuracy Getting ready How to do it See also Using Pyccuracy to verify web app security Getting ready How to do it See also Installing the Robot Framework How to do it Creating a data-driven test suite with Robot Getting ready How to do it What are the best ways to write the code that implements our custom keywords?
Given-When-Then results in duplicate rules Do the try-except blocks violate the idea of keeping things light? See also Tagging Robot tests and running a subset Getting ready How to do it What about documentation? See also Testing web basics with Robot Getting ready How to do it Learn about timing configurations—they may be important! See also Using Robot to verify web app security Getting ready How to do it Why not use a 'remember me' option?
Shouldn't we refactor the first test scenario to use the keyword? Would arguments make the login keyword more flexible? See also Creating a project-level script to verify this chapter's acceptance tests Getting ready How to do it Can we only use getopt? What's wrong with using the various command-line tools? Configuring Jenkins to run Python tests upon commit Getting ready How to do it Do I have to use git for source code management? What is the format of polling? What did teamcity-nose give us?
See also 7.
Installing and running coverage on your test suite How to do it Why are there no asserts in the unit test? What use is an XML report? See also Getting nosy with coverage How to do it Why use the nose plugin instead of the coverage tool directly? Why are sqlite3 and springpython included? Filtering out test noise from coverage How to do it See also Letting Jenkins get nosy with coverage Getting ready How to do it Nose doesn't directly support coverage's XML option Updating the project-level script to provide coverage reports Getting ready How to do it There's more… Can we only use getopt?
Security, checking, and integration aren't smoke tests! Please try again later. Paperback Verified Purchase. Pretty great tutorials and walkthroughs I'm getting the impression that some of the material is slightly outdated though, but I cannot verify that. Regardless the material is great for learning the concepts.
One person found this helpful. Kindle Edition Verified Purchase. I bought the Kindle version recently and am making my way through it. I have learned a number of things from this book and I would recommend it for anyone looking for some examples on testing in Python including useful libraries.
However, the Kindle version is not formatted very well. The code sections are all left aligned making it very difficult to read. This also means that the code needs to be reformatted before running. The Kindle version of this book is broken and there has been no update as of April 11, All the text is justified and the Python code examples striped of significant whitespace, rendering them unreadable without unnecessary and frustrating effort.
I received a digital review copy of this book from the publisher, Packt Publishing. The opinions expressed here are entirely my own.
I recently decided to read through Greg L. Turnquist's new book Python Testing Cookbook. The book promises to give "simple and effective recipes for testing Python code", starting with the most basic testing tool unittest and working into more complex tools like doctest, Nose and the BDD tool Lettuce. The book also touches on some additional topics like code coverage reports, acceptance testing, load testing and configuring tests to run under continuous integration.
The chapter starts with a very quick introduction to BDD and progresses into first recipe, which shows how to perform some simple BDD style tests with a custom Nose plugin.
The chapter then progresses into recipes for doctest, Nose's spec plugin and Lettuce. While the chapter is not going to turn someone into a master of BDD, it is a good introduction to the software that is available for Python developers and contains some good recipes for quickly getting started and utilizing BDD to test an application.
In addition to Chapter 4, I also enjoyed Chapters 6 and 7 quite a bit. Chapter 6 details a few different ways to integrate Python tests with the continuous integration platforms of Jenkins and TeamCity.
These recipes include setting up both services to generate reports, run tests on commit and run tests on a scheduled interval. Chapter 7 is similar, but details how to use the coverage tool with a suite of unit tests. As a whole I enjoyed the book and I thought that I was able to pick up some nice new pointers for testing my Python applications. For these developers, each chapter should provide new information and help to get the developer up-to-speed very quickly.
For someone that is already familiar with Python and Python TDD, I would suggest checking out the table of contents for the book. Each chapter is very helpful and provides a good introduction to the topics discussed in the chapter, however because the chapters are written as introductions, if you are already familiar with the topic for that chapter, its unlikely that you'll pick up much new from the chapter.
For new developer's I'll give the book 4 stars, for experienced developers I'll give the book 3 stars. Turnquist's "Python Testing Cookbook" explores automated testing at all levels, with the intention of providing the reader with the knowledge needed to implement testing using Python tools to improve software quality.
To this end the book presents over 70 "recipes" in its nine chapters ranging from the basics of unit testing, through test suites, user acceptance and web application testing, continuous integration, and methods for smoke- and load-testing , covering both tools for testing Python, and Python tools for testing. It also delivers advice about how to get the most from automated testing, which is as much an art as a science.
The first three chapters introduce the fundamentals: Having established a solid foundation, subsequent chapters look at increasingly broader levels of automated testing using the appropriate relevant Python tools: Later chapters cover higher level concepts and tools, such as using nose to hook Python tests into "continuous integration" servers both Jenkins and TeamCity are covered in detail , and assessing test coverage using the "coverage" tool both as a metric, and to identify areas that need more tests.
A detailed chapter on smoke- and load-testing includes practical advice on developing multiple test suites for different scenarios, and methods for stress-testing for example, by capturing and replaying real world data to discover weaknesses in a system before going to production. The final chapter distils the author's experience into general advice on making testing a successful part of your code development methodology, both for new and legacy projects. There's a lot of good stuff in this book: There is also a lot of excellent and hard-won practical advice from the author's own experience - not only in these early chapters but throughout the book - which is consistently valuable in this regard the final chapter is a real highlight and could easily stand alone - I will definitely be re-reading it soon.
Elsewhere the various tools and topics are presented clearly with plenty of useful detail, and in some cases have demystified things that I'd always assumed were quite esoteric and difficult to do nose in particular was a revelation to me, but also setting up continuous integration servers and measuring test coverage.
There are a few disappointments: