Testing is essential for any activity to succeed; this applies to software as well. It not only covers verification but also the validation of the software. Commonly used testing practices include unit and functional testing that validates the needed functionality. However, manual efforts by testers or stakeholders are required to verify that the software being tested works as desired, as part of the software acceptance process.
As software has grown both in size and amount over the years, the acceptance testing process has become automated, leaving behind vast information in the form of audit trails of tests that need to be made available to the user at a glance and without which the purpose of acceptance test automation is defeated.
The Robot framework provides this missing link that integrates with the testing tool used for performing acceptance testing over the software or the "system under test" and structures this execution into discrete structures of tests arranged in a proper hierarchical manner; this not only results in execution in the desired manner, but also generates orderly reports.
Being an open source framework, this tool does not limit you to simply performing acceptance testing in a hierarchical manner and generating automated reports, but also allows you to customize it and create your own tool or even use it with other tools as part of a custom test environment setup required for your team.
Chapter 1, Getting Started with the Robot Framework, explains the need for acceptance test driven development, offers a high-level overview, and gives different methods of installation of the Robot Framework tool. It also discusses the various commands available for the user to interact with the framework and demonstrates simple test project creation and execution to the user.
Chapter 2, Configuring a Test Project, explains the test hierarchies and covers the different files and tables used in the framework as well as different syntax options to write the test files. It explains the commonly used test practices and explains how information can be re-used within the tests.
Chapter 3, Further Test Customization, covers test re-use, connectivity to external data sources and test templates, and user-defined keywords. It also provides different test writing styles and offers comparison with some other famous behavior-driven test tools.
Chapter 4, Extending the Framework, demonstrates real-world testing challenges and offers practical ways of using various built-in and external libraries to solve these challenges. It covers both the Web-based and desktop testing through object-oriented as well as image-based tests. In the absence of a compatible testing library, it also demonstrates the creation and integration of a custom written library.
Chapter 5, Generating Reports, covers the generated output of the tool in detail and offers different ways to customize the reports as well as the logs generated through the built-in as well as custom mechanisms. It also encourages the user to leverage the source code to create a customized reporting mechanism in the existing framework.
In order to use the Robot Framework, you first need to install Python (the examples have been tested against 2.7, but you are free to use any version between Python 2.7 or 3), which might be installed if you are using a popular Linux distro or Mac OS X as your OS. Apart from Python, you can also install Jython and IronPython that can be used to run Python-based applications and tools such as the Robot Framework on JDK or .NET Framework runtime. A few examples do require explicit use of Jython, but the rest of the available environments can be used with any Python-based runtime. To install third party tools, or for easier installation, it is recommended to use easy_install Python module. Installations for tools/frameworks needed alongside testing with the Robot Framework are provided where necessary.
As this book is focused on a testing tool, it primarily targets the testers. However, given the necessities of creating an in-house flexible testing tool, a hands-on Python development is also needed to modify and develop a testing framework to suit one's needs. Similar to DevOps movement, developers today are seeking ways to look beyond test and behavior-driven development, and the testers are also required to be more productive through means of automation. In this book, you will have to wear both the developer's and tester's hat in various situations in order to gain knowledge of this tool's working from both the perspectives.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "We can include other contexts through the use of the include directive."
A block of code is set as follows:
# This code block runs 5 times for values of x between 11 and 15
| | :FOR | ${x} | IN RANGE | 11 | 16 |
# This code block runs 5 times for x = 10, 12, 14, 16,18
| | :FOR | ${x} | IN RANGE | 10 | 20 | 2 |
| | | Log | ${x} |When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
'<div id="top-right-header">' +
'<div id="report-or-log-link"><a href="#"></a></div>' +
'<div id="my_custom_image"><img src="../Pictures/github-icon.jpg" /></div>'+
'</div>', {
generated: window.output.generatedTimestamp, Any command-line input or output is written as follows:
pybot --randomize tests all_tests
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "On specifying the Fail option, the user is further requested for the failure message before failing the test."
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