Automated testing is a key success factor when you develop mobile applications. In the Android ecosystem, the free Android Studio tool provides many useful features for apps developers. In this article, Nikolay Belousov explores how you can use Android Studio for software testing and explains how to retrieve logs.
Database testing is one of the areas that might have the smaller number of open source tools. The programming languages have many xUnit tools and mocking frameworks, but this is not the case for databases. This article provides a list of open source tools that can be used to perform unit, load and security testing on several relational and NoSQL databases.
Much time can be wasted testing complex OLAP Cubes only to find they weren't created correctly. This articles draws on real-world experience to show how to unit test Cubes to ensure they were built the right way.
Code coverage is a metric that gives the degree to which the source code of a program is tested by a particular test suite. This metric is provided by open source or commercial code coverage tools and displayed in quality dashboards like SonarQube. There are many discussions about the right level of code coverage. In his book Quality Code, Stephen Vance explains the limit of this metric.
The Coding Dojo is a training session where professional programmers practice and improve their skills, and work with Test Driven Development (TDD) in particular. You do Code Kata exercises in a group, play collaborative games, and reflect on not only the code you end up with, but the process you used to create it. I see it as a good way for a group of programmers to sustainably change the way they work, and introduce more effective coding practices.
To test SQL, you need test data. There are usually many reasons why you can't use production data. Although it is usually enough to use a utility to generate test data, sometimes your requirements will compel you to resort to code to supplement this. This article shows how he used SQL and C# to generate large volumes of test data involving related columns and complex distributions.
Creating an effective data migration testing strategy is critical to reducing risk and delivering a successful migration. This article offers thoughts and recommendations on how to create a more consistent data migration testing methodology using either a black box or a white box approach.