Did you ever wonder how testing at Google looks like? What tools we use to help us out and how do we measure and act on test coverage? We will briefly describe the development process at Google, then focus on use of code coverage measurement and how we use code coverage to improve code quality and engineering productivity.
Easy Coverage is an open source framework that can dynamically generate Java unit tests to perform basic verifications. Easy Coverage is extensible and highly configurable. It can work as a standalone product or it can be used with JUnit. In his blog post, Romain Delamare explains how to dynamically generate Java unit tests with Easy Coverage.
It is rather rare to have access to the software testing techniques used by a large project to maintain the quality of its code base. In this blog post, Jan Wloka, a member of the team behind IBM’s Rational Team Concert, presents the different techniques and tools used to control software evolution and to improve the quality of their code base.
In this blog post, Mark Prichard presents a solution on how to use Jenkins to give a “QA dashboard” view of a native Android application build. His goals were to show metrics for the results of unit test and code coverage in an Android build context on the Jenkins continuous integration system.
This article covers the coverage analysis of Python: * Building a network management application * Installing and running coverage on your test suite * Generating an HTML report using coverage * Generating an XML report using coverage * Getting nosy with coverage
Cobertura is an open source tool that measures test coverage by instrumenting a code base and watching which lines of code are and are not executed as the test suite runs.
Code coverage tools measure how thoroughly tests exercise programs. They are misused more often than they’re used well. This paper describes common misuses in detail, then argues for a particular cautious approach to the use of coverage.