An analysis of Mutation testing and Code coverage during progress of projects

In order to deliver high quality software projects, a developing team probably needs a well-developed test suite. There are several methods that aim to evaluate test suites in some way, such as Code coverage and Mutation testing. Code coverage describes the degree of source code that a program executes when running a test suite. Mutation testing measures the test suite effectiveness. More development teams use code coverage to a greater extent than mutation testing. With code coverage being monitored throughout a project, could the development team risk drop of the test suite effectiveness as the codebase getting bigger with each version? In this thesis, a mutation testing tool called PIT is used during progress of four well known open source projects. The reason for this is to show that mutation testing is an important technique to ensure continuously high test suite effectiveness, and does not only rely on code coverage measurements. In general, all projects perform well in both code coverage and test suite effectiveness, with the exception of one project inwhich the test suite effectiveness drops drastically. This drop shows that all projects are at risk of low test suite effectiveness, by not using mutation testing techniques