Going Beyond Code Coverage in Automated Tests

Code coverage is not a good enough metric for the quality of unit tests. Join Lou, as he covers four ways to augment the data. The first is to use code complexity analysis to show areas that are undertested, but that could use testing because they are complex. The second is to use code repository history logs to show areas that are undertested, but have changed a a lot. Lou will also show how to get coverage for just the new code in a pull request. The third is to use analytic data from user usage of your software to show areas that are heavily used, but not tested well. Finally, the session will end with mutation testing. Lou will explain the concept and how it tests not just coverage, but the effectiveness of the test, and demo an open-source tool to show how it works. By the end of the session, you will know why code coverage isn’t a good enough metric and four ways to augment it as well as having experienced demos of open-source tools that can help you with each new technique you have learned.