In this chapter we will cover the following topics:

This book introduces you to the Robot Framework, which is a Python-based, keyword-driven, and acceptance test automation framework. It is very flexible and easy to use and extend according to your intentions. Built to provide acceptance test regardless of the platform size and scope of the software to be tested, also known as System Under Test (SUT), it is an ideal software tool to structure and manage different tests as part of a comprehensive test suite. Since this tool utilizes and manages the tests as well as deals with the software under test, it is beneficial for both testers and developers alike. Today, with the rise of versatile software tools and greater emphasis on test-driven development, the line between tester and developer has become blurred. To learn and use this tool effectively, you will have to put yourself in the shoes of both tester as well as a developer. The Python and/or ports into Java and .Net framework such as, Jython and Ironpython are also required.

As with any other physical creation, software is built with an aim of directly or indirectly changing our lives to solve a task or desire of someone. However, programming is an abstract science that is not dependent upon its end-user's desires, commonly referred to as garbage in – garbage out. A difference between the created software and its expected behavior by its user determines its implementation by the user. Thus, the end user must accept the software that is intended to be sold to him. However, the user often does not wants to be involved in the finer details and just needs to get the things done as he envisages with each iteration of the software. In order to perform this, the interactions that the end user does with the software is needed to be verified beforehand, which has led to creation of testing especially designed to perform this process of testing and verification. This process is known as acceptance testing. However, as the software grows, more and more, acceptance tests come up that give rise to a sense of chaos as upon failure of a test, proper context is not easily identified.

As the scale and complexity of software has grown up, so has the need for its quality assurance. Manual tests often are quite easy to set up, but they give diminishing returns of scale and are only feasible up to an extent, where a tester can manually work through different scenarios and identify bugs and errors in time without affecting the delivery schedule of the resultant product.