Coverage.py vs. JCov vs. Python vs. RT-LAB Comparison Chart

The JCov open-source project is used to gather quality metrics associated with the production of test suites. JCov is being opened in order to facilitate the practice of verifying test execution of regression tests in OpenJDK development. The main motivation behind JCov is the transparency of test coverage metrics. The advantage to promoting standard coverage based on JCov is that OpenJDK developers will be able to use a code coverage tool that stays in the 'lock step' with Java language and VM developments. JCov is a pure java implementation of a code coverage tool that provides a means to measure and analyze dynamic code coverage of Java programs. JCov provides functionality to collect method, linear block, and branch coverage, as well as show uncovered execution paths. It is also able to show a program's source code annotated with coverage information. From a testing perspective, JCov is most useful to determine execution paths.

RT-LAB is OPAL-RT’s real-time simulation software combining performance and enhanced user experience. Fully integrated with MATLAB/Simulink®, RT-LAB offers the most complex model-based design for interaction with real-world environments. It provides the flexibility and scalability to achieve the most complex real-time simulation applications in the automotive, aerospace, power electronics, and power systems industries. Since its first application nearly 20 years ago on the Canadian Space Agency’s Canada Arm, RT-LAB has revolutionized the world of systems engineering, whether in space, on the ground or at sea. RT-LAB enables engineers and scientists to accelerate the development of new prototypes and to meet the most rigorous testing required by new and innovative technologies. RT-LAB handles everything, including code generation, with an easy-to-use interface. With just a few clicks of the mouse, a Simulink® model becomes an interactive real-time simulation application.