Benchmarking plays an important role in the study of evolutionary computation methods and other optimization algorithms. Among other benefits, benchmarking helps us analyze strengths and weaknesses of different techniques — knowledge that can be used to design more efficient optimization approaches. Core to benchmarking is a well-designed experimental setup, which ranges from the selection of algorithms, problem instances, and performance metrics over efficient experimentation to a sound evaluation of the benchmark data. To assist researchers and users of evolutionary computation methods, a number of different tools addressing the various different aspects of benchmarking are available. However, most of them are developed in isolation, without a possible integration to already existing software in mind. This hinders knowledge transfer between the different research groups and between academic and industrial practitioners of evolutionary computation methods.