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One of the main goals of natural language processing (NLP) is synthetic understanding of natural language documents, especially reading comprehension (RC). An obstacle to the further development of RC systems is the absence of a synthetic methodology to analyze their performance. It is difficult to examine the performance of systems based solely on their results for tasks because the process of natural language understanding is complex. In order to tackle this problem, we propose in this paper a methodology inspired by unit testing in software engineering that enables the examination of RC systems from multiple aspects. Our methodology consists of three steps. First, we define a set of prerequisite skills for RC based on existing NLP tasks. We assume that RC capability can be divided into these skills. Second, we manually annotate a dataset for an RC task with information regarding the skills needed to answer each question. Finally, we analyze the performance of RC systems for each skill based on the annotation. The last two steps highlight two aspects: the characteristics of the dataset, and the weaknesses in and differences among RC systems. We tested the effectiveness of our methodology by annotating the Machine Comprehension Test (MCTest) dataset and analyzing four existing systems (including a neural system) on it. The results of the annotations showed that answering questions requires a combination of skills, and clarified the kinds of capabilities that systems need to understand natural language. We conclude that the set of prerequisite skills we define are promising for the decomposition and analysis of RC.