The use of Python is increasing not only in software development, but also in fields such as data analysis, research science, test and measurement, and other industries. The growth of Python in many critical fields also comes with the desire to properly, effectively, and efficiently put software tests in place to make sure the progr
ams run correctly and produce the correct results. In addition, more and more software projects are embracing continuous integration and including an automated testing phase, as release cycles are shortening and thorough manual testing of increasingly complex projects is just infeasible. Teams need to be able to trust the tests being run by the continuous integration servers to tell them if they can trust their software enough to release it.