Abstract: With the development of artificial intelligence technology and the expansion of the IoT concept, the problem of self-adaptability of the system which can respond to a given situation without system intervention is becoming important. Many self-adaptive systems implement self-adaptation to adaptation objects through implicitly or explicitly defined feedback loop processes. Therefore, in the feedback loop process, defining the monitoring elements for the adaptation object, the states of the respective elements and the corresponding plans for the identified states is directly related to realizing the system’s self-adaptability. Existing self-adaptive systems are designed to implement self-adaptability for a specific area. Many studies have proposed a method and framework for implementing a general adaptive self-adaptive system but the complexity that the system must deal with increases as the number of adaptive elements increases. Due to the characteristics of the self-adaptive system it is a very difficult problem to confirm the validity of all adaptation results. In this study, we propose a method to calculate the possible combinations of states and the corresponding plans in the feedback loop of the system to verify the validity of the adaptation results in the self-adaptation process. This methodology defines the adaptation object of a self-adaptive system as a set of elements that can have a finite number of states and then calculates the number of state combinations that can be generated by the elements in each feedback loop and the corresponding plan. The computed result can serve as metadata that can implement a self-adaptive system because it includes complexity, size and operational criteria of the self-adaptive system. It can also be used to determine the suitability of the implemented adaptation results of the adaptive system. We describe the methodology for defining the adaptation process and the membership of the self-adaptive system based on the feedback loop according to the methodology and for calculating the number of cases of state combinations and corresponding plans. In this study, we propose an adaptation system based on the proposed method and compare the result of the proposed method with that of the adaptive system.