Currently, there exists no means of measuring quality and effectiveness of tests developed for GPU kernels. Traditional coverage criteria over CPU programs is not adequate over GPU kernels as it uses a completely different programming model and the faults encountered may be specific to the GPU architecture. In this poster, we present our framework, CLTestCheck, for assessing quality of test suites developed for OpenCL kernels. The framework is able to measure kernel code coverage using three different coverage metrics that are inspired by faults found in real kernel code, seed different types of faults in kernel code and measures fault finding capability of test suites and simulates different work-group schedules to check for potential deadlocks and data races with a given test suite. Case studies using 82 publicly available GPU kernels showed that CLTestCheck is capable of automatically measuring effectiveness of test suites, in terms of kernel code coverage, fault finding and revealing data races in real OpenCL kernels.