Developing a time- and cost-efficient method for second language (L2) oral proficiency measurement is one of the research topics that has attracted much attention in recent decades. The purpose of this study is to develop a computerized oral testing system for L2 Japanese using automatic speech recognition (ASR) technology. Two testing methods called elicited imitation (EI) and simulated speech (SS) are proposed to quantify L2 accuracy and fluency via ASR processing. This study also suggests systematic EI item creation leveraging corpus technology and discusses the effectiveness of the test items created through analyses of item difficulty. Further, refinement of the EI grading system is described through a series of statistical investigations. For SS, this study reports the five most influential L2 fluency features identified through machine learning and proposes a method to yield individual SS scores with these features based on previous studies. Lastly, several methods to combine the EI and SS scores are presented to estimate L2 oral proficiency of Japanese.