Agile testing and test automation are almost mandatory for projects that demand high quality as well as short release cycles like Scrum. It acts as a safety net in order to protect existing functionality against bugs resulting from unintended side-effects of recent changes. Software developers “in the trenches” often automate their tests but don’t practice strict Test-Driven Development (TDD).
Software testing starts when you write the code that will be tested. In these two articles, Shane Tomlinson discusses some anti-patterns that will make your JavaScript frontend code difficult to test. He explains how to detect these anti-patterns in your code and then shows how to refactor your JavaScript to be easier to read, to reuse and to test.
Facebook is released twice a day, and keeping up this pace is at the heart of its culture. With this release pace, automated testing with Selenium is crucial to making sure everything works before being released.
Big Data is a big topic in software development today. When it comes to practice, software testers may not yet fully understand what is exactly Big Data. A tester knows that you need a plan for testing it. Since most Big Data lacks a traditional structure, how does Big Data quality look like? And what the are most appropriate software testing tools? This article tries to answer these questions.
If unit testing has not always been adopted by software developers, this practice has become more mainstream with Agile approaches. Test-Driven Development and Continuous Integration are two technical approaches that require good unit testing. In this blog post, Maxime Lemaitre discusses attitudes that might make your unit testing fail.
With a growing number of instrumented tools in the market, Mobile End-to-End Testing (MOET) uses a non-instrumentation approach, and extensive use of design patterns. MOET’s mobile device libraries are pluggable open-sourced components and only one language and test harness is needed for testing on diverse mobile platforms.
In unit testing, you use mocks to simulate the behavior of other code parts in controlled ways. mock is a Python library for testing that allows you to replace parts of the system under test with mock objects and make assertions about how they have been used. mock has been integrated the Python standard library since version 3.3 under the name unittest.mock.
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